Merge pull request 'Release v26.04.15.1 — Dynamic voice silence threshold + filled mic halo' (#35) from dev into main

The web silence detector previously ran RMS over getByteFrequencyData
bytes (which are already dB-scaled) and re-log'd the result, producing
numbers that didn't line up with real dBFS. Combined with a static
-40 dB threshold that sat right on top of room ambient, silence
detection rarely fired and the user had to click stop manually.

- Rewrite useSilenceDetector to use getFloatTimeDomainData for honest
  linear RMS → dBFS.
- Silence threshold is now dynamic: track session peak dBFS and treat
  "silent" as 15 dB below peak. Auto-calibrates per mic/room.
- Grace period (1500 ms) at start so the user can begin speaking
  before checks arm; static -45 dB fallback until peak clears -25 dB
  so dead-silent sessions don't spin forever.
- Silence duration bumped 1500 → 2000 ms for breathing room.

Visual: detached red radial-gradient disc sits behind the mic in a
wrapper; the button no longer scales, so the white mic icon stays
legible on top while the halo grows from 1x to ~2.6x with live
amplitude. Much more obvious "you are live" signal than the prior
subtle box-shadow pulse.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

.forgejo/workflows/ci.yml

@@ -1,13 +1,27 @@
 # CI runs first; build only proceeds if all checks pass.
 #
-# Push to dev:       typecheck + lint + test → build :dev + :<sha>
-# Tag v* (release):  typecheck + lint + test → build :latest + :<sha> + :<version>
-# Pull request:      no CI run — code is validated on dev push before any PR is opened
+# Push to dev:       typecheck + lint + test + build :dev + :<sha>
+# Tag v* (release):  typecheck + lint + test + build :latest + :<sha> + :<version>
+#
+# main pushes are NOT gated here: a merge to main only happens after
+# dev has already passed CI, and the release tag is the sole trigger
+# for a production image. Re-running CI on the merge commit just burns
+# runner time without changing the outcome.
 #
 # To cut a release:
 #   Create a release via the Forgejo UI on main with a v* tag name.
 #   The tag push triggers this workflow; build job pushes :latest + :<version>.
 #
+# PRs aren't triggered on purpose — this is a solo dev→main flow, so
+# gating on branch push is already enough.
+#
+# NOTE on the `if:` guards below: Forgejo Actions does not consistently
+# honor `on.push.branches` as a filter — merge commits landing on main
+# still trigger the workflow, producing redundant runs on the same SHA
+# that was already gated on dev. Every job therefore repeats the ref
+# check so main pushes trigger the workflow but every job skips
+# immediately (no runner time, no duplicate work).
+#
 # Required secrets (repo → Settings → Secrets → Actions):
 #   REGISTRY_USER   — your Forgejo username
 #   REGISTRY_TOKEN  — Forgejo PAT with write:packages scope
@@ -28,8 +42,18 @@ on:
       - "assets/**"
       - "fable-mcp/**"
       - ".forgejo/workflows/ci.yml"
-  # pull_request trigger intentionally omitted — all changes go through dev
-  # first, where CI already runs on push. PR runs would be redundant duplication.
+
+# Cancel older runs on the same branch when a newer push lands. Tag runs
+# get their own group implicitly (refs/tags/v1.2.3 ≠ refs/heads/dev) and
+# are never cancelled, so a release build can't kill itself mid-flight.
+concurrency:
+  group: ci-${{ github.ref }}
+  cancel-in-progress: ${{ !startsWith(github.ref, 'refs/tags/') }}
+
+# Least-privilege default. Jobs that need more (build pushes to the
+# registry) upgrade explicitly.
+permissions:
+  contents: read
 
 env:
   REGISTRY: git.fabledsword.com
@@ -38,17 +62,25 @@ env:
 jobs:
   typecheck:
     name: TypeScript typecheck
-    runs-on: py3.12-node22
+    # Skip on main merge-commit pushes — see workflow header comment.
+    if: github.ref == 'refs/heads/dev' || startsWith(github.ref, 'refs/tags/v')
+    runs-on: ci-runner
     steps:
       - uses: actions/checkout@v6
 
-      - uses: actions/setup-node@v6
+      # Cache node_modules directly (not the npm download cache).
+      # npm ci still has to extract + link every module even with a
+      # warm download cache, which is where the real time goes. Caching
+      # the output directory lets us skip npm ci entirely on hits.
+      - name: Cache node_modules
+        id: npm-cache
+        uses: actions/cache@v4
         with:
-          node-version: "22"
-          cache: "npm"
-          cache-dependency-path: frontend/package-lock.json
+          path: frontend/node_modules
+          key: node-modules-${{ hashFiles('frontend/package-lock.json') }}
 
       - name: Install dependencies
+        if: steps.npm-cache.outputs.cache-hit != 'true'
         run: npm ci
         working-directory: frontend
 
@@ -58,52 +90,57 @@ jobs:
 
   lint:
     name: Python lint
-    runs-on: py3.12-node22
+    if: github.ref == 'refs/heads/dev' || startsWith(github.ref, 'refs/tags/v')
+    runs-on: ci-runner
     steps:
       - uses: actions/checkout@v6
 
-      - name: Install ruff
-        run: pip install --break-system-packages ruff
-
+      # ruff is pre-installed in the ci-runner base image — no install
+      # step needed, lint runs in ~2s.
       - name: Lint
         run: ruff check src/
 
   test:
     name: Python tests
-    runs-on: py3.12-node22
+    if: github.ref == 'refs/heads/dev' || startsWith(github.ref, 'refs/tags/v')
+    runs-on: ci-runner
     steps:
       - uses: actions/checkout@v6
 
-      # Python 3.12 is pre-installed in the runner-base image (py3.12-node22).
-      - name: Cache pip wheels
-        uses: actions/cache@v3
+      - name: Cache uv packages
+        uses: actions/cache@v4
         with:
-          path: ~/.cache/pip
-          key: pip-${{ hashFiles('pyproject.toml') }}
-          restore-keys: pip-
+          path: ~/.cache/uv
+          key: uv-${{ hashFiles('pyproject.toml') }}
+          restore-keys: uv-
 
       - name: Create virtual environment
-        run: python3.12 -m venv /opt/venv
+        run: uv venv /opt/venv
 
       - name: Install package with dev deps
         run: |
-          /opt/venv/bin/pip install --upgrade pip setuptools wheel
-          /opt/venv/bin/pip install --no-build-isolation http-ece
-          /opt/venv/bin/pip install -e ".[dev]"
+          # http-ece doesn't declare setuptools as a build dep, and uv
+          # creates bare venvs without it. Install setuptools first so
+          # --no-build-isolation can find it.
+          uv pip install --python /opt/venv/bin/python setuptools wheel
+          uv pip install --python /opt/venv/bin/python --no-build-isolation http-ece
+          uv pip install --python /opt/venv/bin/python -e ".[dev]"
 
       - name: Run tests
-        run: /opt/venv/bin/python -m pytest tests/ -v
+        run: /opt/venv/bin/python -m pytest tests/ -q
 
   build:
     name: Build & push image
     needs: [typecheck, lint, test]
     # Build on dev branch pushes and version tag pushes only.
-    # main branch pushes run CI for safety but do not build —
-    # the release tag (v*) is the sole trigger for a production image.
-    if: |
-      github.event_name == 'push' &&
-      (github.ref == 'refs/heads/dev' || startsWith(github.ref, 'refs/tags/'))
-    runs-on: py3.12-node22
+    # Mirrors the ref guard on the gate jobs above — main merge-commit
+    # pushes skip here too, so no production image is ever built from a
+    # raw main push (only from the v* tag the release creates).
+    if: github.ref == 'refs/heads/dev' || startsWith(github.ref, 'refs/tags/v')
+    runs-on: ci-runner
+    permissions:
+      contents: read
+      packages: write
     steps:
       - uses: actions/checkout@v6
 
@@ -122,11 +159,14 @@ jobs:
           echo "build_version=$BUILD_VERSION" >> $GITHUB_OUTPUT
 
       - name: Free disk space
+        # Self-hosted runner housekeeping. Two-step cleanup:
+        # 1. Prune dangling containers/images globally (stops the runner
+        #    from accumulating cruft from past failed builds).
+        # 2. Trim the BuildKit layer cache to a 5GB ceiling so the pip
+        #    mount cache survives but old intermediate layers don't
+        #    accumulate indefinitely.
         run: |
-          # Remove all unused images (including old :SHA tags) and containers.
           docker system prune -af || true
-          # Keep the local BuildKit cache bounded so pip mount cache survives
-          # but stale intermediate layers don't accumulate indefinitely.
           docker builder prune --keep-storage 5g -f || true
 
       - name: Set up Docker Buildx
@@ -147,3 +187,10 @@ jobs:
           provenance: false
           tags: ${{ steps.tags.outputs.value }}
           build-args: BUILD_VERSION=${{ steps.tags.outputs.build_version }}
+          # Registry-backed layer cache. Pull from :cache to prime
+          # BuildKit, push updated layers back to :cache so the next
+          # build starts warm even if the runner's local cache was
+          # pruned. `mode=max` exports all intermediate layers, not
+          # just the final image, which is what gives the ~80% speedup.
+          cache-from: type=registry,ref=${{ env.IMAGE }}:cache
+          cache-to: type=registry,ref=${{ env.IMAGE }}:cache,mode=max,ignore-error=true

alembic/versions/0038_add_rss_item_full_content.py

@@ -0,0 +1,34 @@
+"""Add content_full and context_prepared caches to rss_items.
+
+Revision ID: 0038
+Revises: 0037
+Create Date: 2026-04-13
+"""
+
+from __future__ import annotations
+
+import sqlalchemy as sa
+from alembic import op
+
+revision = "0038"
+down_revision = "0037"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.add_column("rss_items", sa.Column("content_full", sa.Text(), nullable=True))
+    op.add_column(
+        "rss_items",
+        sa.Column("context_prepared", sa.Text(), nullable=True),
+    )
+    op.add_column(
+        "rss_items",
+        sa.Column("content_fetched_at", sa.DateTime(timezone=True), nullable=True),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("rss_items", "content_fetched_at")
+    op.drop_column("rss_items", "context_prepared")
+    op.drop_column("rss_items", "content_full")

alembic/versions/0039_add_rss_item_discussion_note.py

@@ -0,0 +1,38 @@
+"""Link rss_items to their discussion-summary note.
+
+Revision ID: 0039
+Revises: 0038
+Create Date: 2026-04-14
+"""
+
+from __future__ import annotations
+
+import sqlalchemy as sa
+from alembic import op
+
+revision = "0039"
+down_revision = "0038"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "rss_items",
+        sa.Column("discussion_note_id", sa.BigInteger(), nullable=True),
+    )
+    op.create_foreign_key(
+        "fk_rss_items_discussion_note_id",
+        "rss_items",
+        "notes",
+        ["discussion_note_id"],
+        ["id"],
+        ondelete="SET NULL",
+    )
+
+
+def downgrade() -> None:
+    op.drop_constraint(
+        "fk_rss_items_discussion_note_id", "rss_items", type_="foreignkey"
+    )
+    op.drop_column("rss_items", "discussion_note_id")

docs/2026-04-10-agentic-briefing-design.md

@@ -0,0 +1,216 @@
+# Agentic Briefing — Design Spec
+
+**Date:** 2026-04-10
+**Status:** Proposed
+**Author:** bvandeusen + Claude
+
+---
+
+## Problem
+
+The current briefing pipeline hallucinates calendar events, tasks, and news items that do not exist in the user's actual data. Observed examples from production:
+
+- A morning briefing asserting "your dentist appointment is still in progress" when no such event existed
+- An 8am check-in mentioning "a quick meeting at 10:30 AM" with no backing calendar entry
+- A midday check-in inventing "a team huddle at 2:30 PM" and "the Q2 budget draft due by Friday"
+- The model calling `search_images` in response to a clarifying question about a fabricated meeting
+
+These are not bugs in data retrieval — the data gathering code (`_gather_internal`, `_gather_external`) returns correct values. They are a structural consequence of how the briefing context is assembled.
+
+## Root cause — the "no receipt" problem
+
+The current `run_compilation` pipeline does this:
+
+1. Python code gathers data (tasks, events, weather, news) from the database
+2. Python formats the data into a structured text blob as the user-role message: `TODAY'S EVENTS: ...`, `DUE TODAY: ...`, etc.
+3. The LLM is called **once** with `[system_prompt, user_prompt]` and produces prose
+4. Only the prose reply is written to the conversation. **The underlying data is never persisted in the conversation history.**
+
+When the user later chats in the briefing conversation, the chat endpoint loads the full conversation history — which contains the model's *prose* from earlier but not the data that prose was derived from. The model's own prior output becomes the only source of "truth" available for follow-up questions. If that prose asserted a fact (real or hallucinated), the model has no way to distinguish it from ground truth when generating the next reply, and it will double down.
+
+Compounding factors:
+
+- **Empty sections are silently omitted.** If `calendar_events` is empty, the user prompt contains no `TODAY'S EVENTS:` line at all. The model has no explicit "zero events" signal — combined with an imperative system prompt ("note calendar events and tasks"), it interprets the silence as "I should mention some" and fabricates.
+- **Scheduled slot injections append synthetic turns.** `run_slot_injection` writes a fake `[Midday briefing update]` user message and the assistant reply into the persistent conversation. By evening, the chat history contains three separate briefings, each potentially with errors, all treated as equal-weight context on follow-up.
+- **The `search_images` tool is available during briefing chat**, with a negative-instruction description ("Not for factual questions"). Small and mid-sized models frequently ignore negative guidance in tool descriptions and call the tool anyway.
+
+## Solution — agentic briefing (the receipt model)
+
+Replace the one-shot synthesis with a tool-call loop. The briefing is no longer "a text blob synthesized from pre-gathered data." It is **a scheduled agent run**: the LLM is given a system prompt, a curated set of read-only data tools, and a trigger ("generate the morning briefing"). The model must call tools to see what exists. Every tool call and tool result becomes part of the conversation history, where it lives as a permanent, structured receipt.
+
+### Why this fixes the hallucination
+
+When the model calls `list_events(today) → []`, that empty array is now a persistent message in the conversation. On a follow-up question like "what meeting?", the chat endpoint loads the conversation and the model sees its own tool result from the morning showing no events. Answering "you had a meeting at 10:30" would require the model to directly contradict a tool result sitting two messages back — something LLMs are much more reliable at avoiding than contradicting their own prior prose.
+
+In short: **the model cannot fabricate what has a visible receipt proving it does not exist.**
+
+### What the model sees — before vs after
+
+**Before** — the model sees one structured blob and produces prose. The blob is discarded.
+
+```
+[system] You are a personal assistant... weave together what matters...
+[user]   Date: 2026-04-10
+         WEATHER: Home — partly cloudy, 8–16°C
+         DUE TODAY: Fix briefing hallucinations
+         OVERDUE (2 tasks): ...
+[assistant] Morning! Looks like a quiet day with two overdue items...
+```
+
+**After** — the model is given tools and iterates to ground truth.
+
+```
+[system] You are the user's personal assistant giving their morning briefing.
+         Use tools to see what's relevant. Only mention things you learned from
+         tool calls. If a tool returns nothing, say so honestly.
+[user]   Generate the morning briefing.
+[assistant] tool_call: list_tasks(filter="due_today")
+[tool]   [{"id": 81, "title": "Fix briefing hallucinations", ...}]
+[assistant] tool_call: list_tasks(filter="overdue")
+[tool]   [{"id": 42, ...}, {"id": 43, ...}]
+[assistant] tool_call: list_events(today)
+[tool]   []
+[assistant] tool_call: get_weather(location="home")
+[tool]   {"description": "partly cloudy", "temp_min": 8, "temp_max": 16}
+[assistant] tool_call: get_rss_items(max=8)
+[tool]   [...]
+[assistant] Morning! Partly cloudy today, 8 to 16 — nothing on the calendar
+         so it's a clean run at the desk. Two things to keep in mind...
+```
+
+The conversation now contains verifiable receipts: `list_events(today)` returned `[]`, and that result sits in context forever (until pruned). Follow-up questions operate against those receipts.
+
+## Architecture
+
+### Existing infrastructure (reused, not rebuilt)
+
+The codebase already has the agentic primitives — they're used for regular chat:
+
+- **`llm.py::stream_chat_with_tools`** — the streaming tool-use loop that talks to Ollama with a `tools` parameter and yields tool-call chunks
+- **`generation_task.py::_stream_with_retry`** — wraps `stream_chat_with_tools` with retry-on-500 behavior for cold-model races
+- **`tools.py`** — defines 40+ tool schemas and an execution dispatcher
+
+The briefing bypasses all of this and calls a one-shot `_llm_synthesise` helper. The refactor is mainly "route briefings through the same pipeline regular chat already uses."
+
+### New modules
+
+**`briefing_tools.py`** — a small wrapper exposing a curated read-only subset of `tools.py` for briefing runs. This is an **explicit allowlist**, not a blocklist, so newly-added tools must be opted in:
+
+| Tool | Purpose |
+|---|---|
+| `list_tasks` (with filter args) | See what's actionable today, overdue, high priority |
+| `list_events` (today / upcoming) | Know what's on the calendar |
+| `get_weather` | Current/forecast weather |
+| `get_rss_items` | Pull news themes filtered by user preferences |
+| `list_projects` | Understand project context |
+| `search_projects` | Surface active project summaries |
+| `list_notes` (recent) | Capture follow-ups from yesterday |
+
+**Explicitly omitted** from the briefing tool set:
+
+- `search_images`, `search_web`, `research_topic`, `read_article` — external search is not a briefing concern, and `search_images` is the source of the "Peter Kyle Science Secretary" image-search bug
+- All `create_*`, `update_*`, `delete_*` — briefings are read-only; a scheduled background job must not decide to mutate the user's data on its own
+- `set_rag_scope`, `calculate` — not relevant to briefing content
+
+### New briefing function
+
+**`briefing_pipeline.py::run_agentic_briefing(user_id, slot, model, conversation_id)`** replaces `run_compilation`'s body (and eventually `run_slot_injection`). Internally:
+
+1. Build a slot-specific system prompt (see below)
+2. Load the curated briefing tools from `briefing_tools.py`
+3. Seed messages with `[system, user]` where the user message is a simple trigger like `"Generate the morning briefing."`
+4. Enter a tool-call loop (max 8 iterations):
+   - Call `stream_chat_with_tools`
+   - If the model returns `tool_calls`, execute them via the existing dispatcher, append tool results, continue
+   - If the model returns a final assistant message with no pending tool calls, break
+5. Return `(final_prose, full_message_list, metadata)`
+
+The full message list is important: it's written to the conversation along with the final prose, so the tool-call receipts become part of the persistent record.
+
+### Slot-specific system prompts
+
+Compilation (full morning briefing):
+
+```
+You are the user's personal assistant giving their full morning briefing.
+Use the tools available to see what's actually relevant today — tasks due,
+overdue items, events on the calendar, weather, news themes, project state
+— and weave it into a warm, natural-sounding summary.
+
+Rules:
+- Call tools to see the data. Never assert facts you didn't learn from a tool.
+- If a tool returns nothing (no events today, no overdue tasks), say so
+  honestly. Don't fabricate items to fill space.
+- Write flowing prose. No markdown, no headers, no bullet points.
+- Aim for 6–10 sentences. Skip topics that have nothing interesting.
+- Close on one or two concrete, actionable suggestions.
+
+User profile (for tone and preferences):
+{profile_body}
+```
+
+Check-ins (midday, afternoon):
+
+```
+You are the user's personal assistant giving a brief {slot} check-in.
+Use tools to see what's changed since this morning. Focus on progress
+and what's still unaddressed.
+
+Rules:
+- Call tools to see current state. Never assert facts without tool results.
+- If nothing meaningful has changed, say so briefly — don't invent progress.
+- 3–5 sentences, natural prose, no markdown.
+```
+
+### Conversation hygiene — removing the fake user messages
+
+The current scheduler appends two fake messages on every slot injection:
+
+```python
+await post_message(conv.id, "user", f"[{slot.title()} briefing update]")
+await post_message(conv.id, "assistant", text)
+```
+
+Under the agentic model, we drop the fake `"user"` message entirely. Slot updates become plain assistant messages tagged with `metadata.briefing_slot = "midday"`. The chat endpoint's message loader is updated to filter these when building the LLM context on follow-ups, so a user chatting in the briefing conversation doesn't see three earlier briefings smashed into their history. The UI continues to show them as visible timeline entries.
+
+(This is the Option A filter from the earlier discussion — a small, surgical change compared to migrating briefings to a separate sidecar table. Sidecar storage remains a possible future step.)
+
+### Ollama setup compatibility
+
+The existing Ollama deployment uses non-parallel mode across two GPUs, with a concern about context duplication between cards. This is the correct setup for agentic briefings:
+
+- Each tool-call iteration shares the same KV cache on the same GPU, so appending tool results is cheap
+- There is no race where a second iteration could land on a different card with a different cache state
+- The trade-off is serialization: a briefing in progress will block a concurrent user chat request until it finishes, but scheduled briefings are rare enough (4× per day) that this is acceptable
+
+If GPU contention becomes a problem later, the right lever is pinning specific models to specific GPUs (e.g., background tasks on GPU 1, interactive models on GPU 0) — not enabling parallelism.
+
+## Cost & trade-offs
+
+- **Latency:** a briefing now makes 5–7 inference calls (one per tool-call decision plus the final prose) instead of 1. On a local Ollama with a 7B model, expect 15–40s per briefing vs the current 5–10s. Acceptable for a background job; if it becomes painful at the user-facing check-in slots, investigate letting the model batch independent tool calls in a single turn (Ollama supports this).
+- **Model requirements:** the default model must be reliable at tool calling. `qwen2.5:7b`, `llama3.1:8b`, `mistral-small-3:24b`, and similar handle it well. Models in the ≤3B class typically fail — they either emit no tool calls and return empty prose, or hallucinate invalid tool arguments. If a user's `default_model` is too small, agentic mode should fall back to legacy mode with a log warning.
+- **Context growth:** tool results bloat the conversation. At 8 tool calls per compilation × 4 slots per day, a daily briefing conversation can reach 20-30 KB of JSON-heavy history. Fine for a day; aged briefings should be archived/rolled up after 7 days.
+- **Tool subset drift:** someone adds a new mutating tool to `tools.py` and forgets to update the briefing allowlist. Mitigated by the allowlist model — the default for new tools is "not exposed to briefings."
+- **Infinite loop safety:** a buggy model could tool-call forever. Hard cap at 8 iterations, log a warning if hit, return whatever prose was last produced (or a fallback message).
+
+## Migration path
+
+Ship incrementally, each step independently reversible:
+
+**PR 1 — Agentic compilation behind a feature flag.** Add `briefing_tools.py`, add `run_agentic_briefing`, add a per-user setting `briefing_mode: "legacy" | "agentic"` (default legacy). Route only the 4am compilation through the new path when the flag is set. Keep slot-injection on the legacy path. Enable the flag on the author's account first, validate output quality over several days, then flip the default for all users. No DB migration required — the setting lives in the existing `settings` table.
+
+**PR 2 — Agentic slot injections + conversation hygiene.** Migrate midday/afternoon check-ins to the same pipeline. Remove the fake `[Midday briefing update]` user-role message; slot updates become plain assistant messages tagged with `metadata.briefing_slot`. Add a chat-message-loader filter that excludes slot-tagged messages from the LLM context on follow-ups (they remain visible in the UI).
+
+**PR 3 — UI polish.** Collapsed tool-call status row in the briefing card ("✓ checked calendar · ✓ looked at tasks · ✓ pulled weather"), expanding to show tool results on click. Tool-result cards (weather, news, task list) rendered inline where useful.
+
+**PR 4 (optional) — Sidecar storage for briefing snapshots.** If the chat-filter approach in PR 2 feels too hacky, migrate briefings out of the conversations table and into a dedicated `briefing_snapshots` table. Frontend renders them as pinned timeline cards separate from chat. Larger refactor; defer until PR 1–3 prove the approach works.
+
+## Secondary win — tightening the main chat system prompt
+
+The regular chat is already agentic (it uses `stream_chat_with_tools`), but its system prompt does not explicitly require the model to ground factual claims in tool results. The prompt discipline introduced for briefings — *"Never assert facts you didn't learn from a tool. If a tool returns nothing, say so honestly."* — is worth applying to the main chat system prompt in a follow-up PR. The mechanism already works; only the framing needs tightening.
+
+## Out of scope
+
+- The Android Flutter client's failure to render `search_images` tool-result cards. This is a separate rendering gap in the mobile app and does not affect the server-side fix. Tracked separately.
+- Re-evaluating the Ollama parallelism setting. The current non-parallel config is correct for this work.
+- Replacing the background model for title/summary/observation extraction. That model's role is unrelated to briefings.

docs/architecture.md

@@ -212,8 +212,8 @@ Permission resolution is centralised in `services/access.py`. `get_project_permi
 | `services/api_keys.py` | `generate_key()`, `create_api_key()`, `list_api_keys()`, `revoke_api_key()`, `lookup_key()` (SHA-256 hash lookup) |
 | `services/llm.py` | `build_context()`, RAG injection, history summarisation, `stream_chat_with_tools()`, URL fetching, SSRF guard |
 | `services/generation_task.py` | `run_generation()` — full chat pipeline: intent routing, tool loop, SSE fan-out, push notification; `run_assist_generation()` |
-| `services/intent.py` | `classify_intent()` — fast non-streaming LLM call; intent skip heuristic; `_PRIOR_WORK_REFS` fast-path |
-| `services/tools.py` | All LLM tool definitions + `execute_tool(user_id, tool_name, arguments, conv_id=None, workspace_project_id=None)` dispatcher; duplicate guards; `_resolve_project()` 4-step lookup; `search_projects` and `set_rag_scope` tools |
+| ~~`services/intent.py`~~ | Removed — intent routing eliminated; the main model handles all tool routing directly |
+| `services/tools/` | LLM tool package — decorator-based registry (`_registry.py`), shared helpers (`_helpers.py`), and one module per domain: `notes.py` (create/update/delete/search/list/read), `tasks.py` (list/log_work), `entities.py` (save_person/save_place/lists), `projects.py`, `calendar.py`, `web.py`, `rag.py`, `profile.py`, `rss.py`, `weather.py`, `utility.py` (calculate). `execute_tool()` and `get_tools_for_user()` are the public API. |
 | `services/projects.py` | Project CRUD + `generate_project_summary()` (Ollama, fire-and-forget) + `backfill_project_summaries()` (startup) |
 | `services/embeddings.py` | `upsert_note_embedding()`, `semantic_search_notes(orphan_only=False)` (pgvector cosine similarity) |
 | `services/generation_buffer.py` | In-memory SSE event buffer; `cancel_event`; 60s cleanup; supports both chat (int keys) and assist (string keys) |
@@ -231,7 +231,7 @@ Permission resolution is centralised in `services/access.py`. `get_project_permi
 | `services/briefing_scheduler.py` | APScheduler `BackgroundScheduler`; slots with catch-up logic; async-safe via `asyncio.create_task` |
 | `services/briefing_conversations.py` | Briefing conversation persistence and history queries |
 | `services/briefing_profile.py` | Per-user profile note that the assistant updates over time |
-| `services/research.py` | SearXNG research pipeline: 5 sub-queries → parallel fetch → synthesis; `search_images` for image category |
+| `services/research.py` | SearXNG research pipeline: sub-queries → parallel fetch → outline → section synthesis → executive summary → index note with linked section notes |
 | `services/events.py` | Internal events CRUD: `list_events`, `create_event`, `update_event`, `delete_event`, `get_event`; source of truth for all event LLM tools |
 | `routes/events.py` | `/api/events` — event CRUD routes |
 | `services/caldav.py` | Optional CalDAV sync — user-configured external server; syncs to/from internal store via `caldav_uid` FK; `is_caldav_configured()` guards tool activation |
@@ -291,7 +291,7 @@ Permission resolution is centralised in `services/access.py`. `get_project_permi
 | `services/access.py` | Permission resolution for all shared resources |
 | `services/llm.py` | `build_context()`, RAG injection, history summarisation |
 | `services/generation_task.py` | SSE streaming, tool-call loop, GenerationBuffer management |
-| `services/tools.py` | All LLM tool implementations (`create_note`, `search_notes`, `get_weather`, …) |
+| `services/tools/` | LLM tool implementations (38 tools across 11 modules); decorator-based registry |
 | `services/embeddings.py` | `upsert_note_embedding()`, `semantic_search_notes()` |
 | `services/briefing_pipeline.py` | Two-lane parallel gather → LLM synthesis → briefing output |
 | `services/briefing_scheduler.py` | APScheduler integration, catch-up logic for missed slots |
@@ -311,26 +311,22 @@ See [sso-oauth.md](sso-oauth.md) for provider-specific setup instructions.
 
 ## LLM Pipeline Internals
 
-### Intent Routing
+### Tool Routing
 
-Before the main model runs, a lightweight intent classifier (`services/intent.py`) runs concurrently with `build_context()`. It makes a fast non-streaming call using a smaller dedicated model (`OLLAMA_INTENT_MODEL`, default `qwen2.5:7b`) to determine if the message requires a tool call.
-
-**Skip heuristic** — Intent classification is skipped entirely for short messages (≤10 words) with no action/object keywords, saving 400–800ms on conversational replies.
-
-**Prior-work fast-path** — `_PRIOR_WORK_REFS` regex detects phrases like "research you did", "note you made", "using your research" and returns no-tool immediately, preventing `search_web` from firing when the user references existing notes.
-
-If a tool is detected, the intent's one-sentence `ack` field is streamed as the first chunk (TTFT), the tool executes, then the main model generates a follow-up with the tool result. For chat-only responses the main model streams directly.
+No separate intent router — the main model handles all tool routing directly via Ollama's structured tool-calling output. The model receives the full tool schema list and decides whether to call a tool or respond conversationally. A thinking-mode heuristic (`_should_think()`) detects complex prompts and enables extended reasoning.
 
 ### Tool Loop
 
-Multi-round tool loop (max 5 rounds). All implementations in `services/tools.py`; `execute_tool(user_id, tool_name, arguments, conv_id=None, workspace_project_id=None)` is the dispatcher. `conv_id` and `workspace_project_id` are threaded in from `run_generation()` so tools like `set_rag_scope` can write to the current conversation.
+Multi-round tool loop (max 5 rounds). All implementations in `services/tools/` (decorator-based registry); `execute_tool(user_id, tool_name, arguments, conv_id=None, workspace_project_id=None)` is the dispatcher. `conv_id` and `workspace_project_id` are threaded in from `run_generation()` so tools like `set_rag_scope` can write to the current conversation.
 
-**Duplicate protection on `create_note` / `create_task`:**
+**Unified `create_note` tool** — creates both notes and tasks. Setting `status` (e.g. `"todo"`) creates a task; omitting it creates a knowledge note. All task fields (due_date, priority, milestone, parent_task, recurrence_rule) are available on the single tool.
+
+**Duplicate protection on `create_note`:**
 1. Exact title match (case-insensitive) → hard block, redirect to `update_note`
 2. Fuzzy title match (SequenceMatcher ≥ 82%; punctuation stripped before candidate search) → hard block
-3. Semantic content similarity (threshold 0.90, body ≥ 200 chars) → soft block with `requires_confirmation: true`
+3. Semantic content similarity (threshold 0.90, body ≥ 80 chars) → soft block with `requires_confirmation: true`
 
-**Project resolution** (`_resolve_project`): 4-step lookup — (1) exact DB match, (2) `query in title` substring, (3) `title in query` reverse substring, (4) SequenceMatcher ≥ 0.55.
+**Project resolution** (`_helpers.resolve_project`): 4-step lookup — (1) exact DB match, (2) `query in title` substring, (3) `title in query` reverse substring, (4) SequenceMatcher ≥ 0.55.
 
 ### Context Window and Summarisation
 
@@ -344,8 +340,11 @@ History summarisation threshold: 30 messages. Keeps 8 recent messages. Summary m
 1. Intent model generates 5 focused sub-queries
 2. All 5 SearXNG queries run in parallel (200ms stagger to avoid rate limiter)
 3. Up to 15 unique URLs fetched in parallel
-4. Up to 12 sources passed to synthesis LLM
-5. Result saved as a note with `tags=["research"]`
+4. LLM generates an outline (2–8 sections with title + focus)
+5. Each section synthesised in parallel from relevant sources
+6. Executive summary generated from all section content
+7. Index note created with executive summary + links to section notes; section notes linked back via `parent_id`
+8. Falls back to single-note synthesis if outline generation fails
 
 SearXNG tip: add the app server IP to `botdetection.ip_lists.pass_ip` in SearXNG `settings.yml` to bypass the rate limiter for trusted backend requests.
 

docs/features.md

@@ -68,7 +68,7 @@ Full conversation history with SSE streaming. Features:
 
 ## Web Research
 
-The assistant can search the web (SearXNG) and fetch pages, synthesising findings into notes. A lightweight `search_web` tool answers quick questions inline without saving. Requires `SEARXNG_URL` to be configured.
+The assistant can search the web (SearXNG) and fetch pages, synthesising findings into a structured multi-note research output: an index note with an executive summary and links to focused section notes. Each section covers a distinct aspect of the topic with cited sources. Falls back to a single note when outline generation fails. A lightweight `search_web` tool answers quick questions inline without saving. Requires `SEARXNG_URL` to be configured.
 
 ## Calendar
 

fable-mcp/fable_mcp/server.py

@@ -27,7 +27,7 @@ The hierarchy is: Project → Milestone → Task/Note.
 
 - **Tasks** belong to a project and optionally a milestone. They support sub-tasks via `parent_id`.
   - Status values: `todo`, `in_progress`, `done`, `cancelled`
-  - Priority values: `low`, `normal`, `high`
+  - Priority values: `none` (default), `low`, `medium`, `high`
 
 - **Notes** are free-form markdown documents. They can belong to a project or be standalone
   (orphan notes). Orphan notes are included in the default RAG scope for chat conversations.
@@ -241,7 +241,7 @@ async def fable_create_task(
         title: Task title (required).
         body: Markdown description / notes for the task.
         status: Initial status — one of: todo (default), in_progress, done, cancelled.
-        priority: One of: low, normal, high. Omit for no priority.
+        priority: One of: low, medium, high. Omit for no priority (defaults to "none").
         project_id: Associate with a project (0 = no project).
         milestone_id: Place within a project milestone (0 = no milestone).
         parent_id: Make this a sub-task of another task (0 = top-level).
@@ -280,7 +280,7 @@ async def fable_update_task(
         title: New title, or omit to leave unchanged.
         body: New markdown body, or omit to leave unchanged.
         status: New status — one of: todo, in_progress, done, cancelled.
-        priority: New priority — one of: low, normal, high.
+        priority: New priority — one of: none, low, medium, high.
         project_id: New project (0 = remove from project). Omit to leave unchanged.
         milestone_id: New milestone (0 = remove from milestone). Omit to leave unchanged.
     """
@@ -628,6 +628,128 @@ async def fable_remove_rss_feed(feed_id: int) -> dict:
         return await briefing.remove_rss_feed(client, feed_id=feed_id)
 
 
+# ---------------------------------------------------------------------------
+# Briefing introspection & control
+# ---------------------------------------------------------------------------
+
+
+@mcp.tool()
+async def fable_list_briefings() -> dict:
+    """List the user's briefing conversations, newest first.
+
+    Each briefing has an associated date and conversation id. Use
+    ``fable_get_briefing_messages`` or ``fable_get_conversation`` with
+    the id to pull the actual briefing text and tool-call receipts.
+
+    Returns a dict with a ``conversations`` list.
+    """
+    async with FableClient() as client:
+        return await briefing.list_briefing_conversations(client)
+
+
+@mcp.tool()
+async def fable_get_today_briefing() -> dict:
+    """Fetch today's briefing conversation, creating it if needed.
+
+    Returns the full conversation object including all messages — the
+    scheduled briefing assistant turns, any tool calls the agentic path
+    made, and any chat replies the user has sent in the briefing thread.
+
+    Use this to inspect what a briefing actually said (and what tool
+    results grounded it) without having to query by id.
+    """
+    async with FableClient() as client:
+        return await briefing.get_today_briefing(client)
+
+
+@mcp.tool()
+async def fable_get_briefing_messages(conversation_id: int) -> dict:
+    """Fetch all messages for a specific briefing conversation.
+
+    Args:
+        conversation_id: The briefing conversation id from fable_list_briefings.
+
+    Returns a dict with a ``messages`` list. Each message includes
+    role, content, tool_calls (with results), and metadata — the
+    metadata carries ``briefing_slot`` tags on agentic briefing turns.
+    """
+    async with FableClient() as client:
+        return await briefing.get_briefing_messages(
+            client, conversation_id=conversation_id,
+        )
+
+
+@mcp.tool()
+async def fable_trigger_briefing(slot: str = "compilation") -> dict:
+    """Manually run a briefing slot for the current user.
+
+    Fires the same data refresh the scheduler does (RSS, weather),
+    runs the agentic briefing pipeline, and writes the result into
+    today's briefing conversation. Use this to test prompt changes
+    without waiting for the next scheduled slot.
+
+    Args:
+        slot: One of ``compilation`` (full morning, default), ``morning``,
+              ``midday``, or ``afternoon``.
+
+    Returns a dict with ``conversation_id``, ``message_id``, and ``slot``.
+    """
+    async with FableClient() as client:
+        return await briefing.trigger_briefing(client, slot=slot)
+
+
+@mcp.tool()
+async def fable_reset_today_briefing(run_compilation: bool = True) -> dict:
+    """Wipe today's briefing and (optionally) regenerate from scratch.
+
+    Deletes every message in today's briefing conversation — the
+    conversation row itself is kept so its id stays stable for any
+    open UI sessions. If ``run_compilation`` is True (the default),
+    immediately fires the compilation slot afterward so a fresh
+    briefing lands in place of the deleted content.
+
+    Use this when iterating on briefing prompts or tools and you want
+    to start from a clean slate rather than append another slot update
+    on top of stale output.
+
+    Args:
+        run_compilation: When True, fire ``POST /api/briefing/trigger``
+            for the ``compilation`` slot immediately after wiping.
+            Set False to only wipe without regenerating.
+
+    Returns a dict with ``reset`` (the delete result: deleted count +
+    conversation id) and ``triggered`` (the new message payload, or
+    null if regeneration was skipped).
+    """
+    async with FableClient() as client:
+        return await briefing.reset_today_briefing(
+            client, run_compilation=run_compilation,
+        )
+
+
+# ---------------------------------------------------------------------------
+# Generic conversation access
+# ---------------------------------------------------------------------------
+
+
+@mcp.tool()
+async def fable_get_conversation(conversation_id: int) -> dict:
+    """Fetch any conversation (chat or briefing) with its full message list.
+
+    Returns conversation metadata plus an ordered ``messages`` array.
+    Each message includes role, content, tool_calls (with results),
+    context_note_id, and msg_metadata. Tool calls are in the stored
+    flat format: ``[{"function": name, "arguments": {...}, "result": {...}}]``.
+
+    Useful for debugging agentic briefings, inspecting chat history,
+    or verifying that a tool actually ran with the expected arguments.
+    """
+    async with FableClient() as client:
+        return await briefing.get_conversation(
+            client, conversation_id=conversation_id,
+        )
+
+
 # ---------------------------------------------------------------------------
 # Entry point
 # ---------------------------------------------------------------------------

fable-mcp/fable_mcp/tools/briefing.py

@@ -1,4 +1,4 @@
-"""MCP tools for Fable RSS feed management."""
+"""MCP tools for Fable briefings and RSS feed management."""
 from __future__ import annotations
 
 from typing import Any
@@ -6,6 +6,8 @@ from typing import Any
 from fable_mcp.client import FableClient
 
 
+# ── RSS feeds ────────────────────────────────────────────────────────────────
+
 async def list_rss_feeds(client: FableClient) -> dict[str, Any]:
     """List the user's RSS feeds."""
     return await client.get("/api/briefing/feeds")
@@ -30,3 +32,67 @@ async def add_rss_feed(
 async def remove_rss_feed(client: FableClient, *, feed_id: int) -> dict[str, Any]:
     """Remove an RSS feed by ID."""
     return await client.delete(f"/api/briefing/feeds/{feed_id}")
+
+
+# ── Briefings ────────────────────────────────────────────────────────────────
+
+async def list_briefing_conversations(client: FableClient) -> dict[str, Any]:
+    """List the user's briefing conversations, newest first."""
+    return await client.get("/api/briefing/conversations")
+
+
+async def get_today_briefing(client: FableClient) -> dict[str, Any]:
+    """Fetch today's briefing conversation with all messages."""
+    return await client.get("/api/briefing/conversations/today")
+
+
+async def get_briefing_messages(
+    client: FableClient,
+    *,
+    conversation_id: int,
+) -> dict[str, Any]:
+    """Fetch messages for a specific briefing conversation."""
+    return await client.get(f"/api/briefing/conversations/{conversation_id}/messages")
+
+
+async def trigger_briefing(
+    client: FableClient,
+    *,
+    slot: str = "compilation",
+) -> dict[str, Any]:
+    """Manually trigger a briefing slot — fires data refresh and runs the pipeline.
+
+    Slot is one of: compilation (full morning), morning, midday, afternoon.
+    """
+    return await client.post("/api/briefing/trigger", json={"slot": slot})
+
+
+async def reset_today_briefing(
+    client: FableClient,
+    *,
+    run_compilation: bool = True,
+) -> dict[str, Any]:
+    """Delete all messages in today's briefing and optionally regenerate.
+
+    Wipes the messages in today's briefing conversation (keeping the
+    conversation row), then, if ``run_compilation`` is True, fires the
+    compilation slot so a fresh briefing lands in its place.
+    """
+    reset = await client.post("/api/briefing/reset-today")
+    if not run_compilation:
+        return {"reset": reset, "triggered": None}
+    triggered = await client.post(
+        "/api/briefing/trigger", json={"slot": "compilation"}
+    )
+    return {"reset": reset, "triggered": triggered}
+
+
+# ── Generic conversations ───────────────────────────────────────────────────
+
+async def get_conversation(
+    client: FableClient,
+    *,
+    conversation_id: int,
+) -> dict[str, Any]:
+    """Fetch any conversation (chat or briefing) with all messages and tool calls."""
+    return await client.get(f"/api/chat/conversations/{conversation_id}")

fable-mcp/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "fable-mcp"
-version = "0.2.4"
+version = "0.2.6"
 description = "MCP server for Fabled Assistant"
 requires-python = ">=3.12"
 dependencies = [

frontend/src/api/client.ts

@@ -426,7 +426,9 @@ export async function deleteRssReaction(rssItemId: number): Promise<void> {
   return apiDelete(`/api/briefing/rss-reactions/${rssItemId}`);
 }
 
-export async function openArticleInChat(itemId: number): Promise<{ conversation_id: number }> {
+export async function openArticleInChat(
+  itemId: number,
+): Promise<{ conversation_id: number; assistant_message_id?: number; status?: string }> {
   return apiPost(`/api/chat/from-article/${itemId}`, {});
 }
 

frontend/src/components/BriefingToolStatusRow.vue

@@ -0,0 +1,168 @@
+<script setup lang="ts">
+import { computed, ref } from "vue";
+import ToolCallCard from "@/components/ToolCallCard.vue";
+import type { ToolCallRecord } from "@/types/chat";
+
+const props = defineProps<{
+  toolCalls: ToolCallRecord[];
+}>();
+
+const expanded = ref(false);
+
+function shortName(fn: string): string {
+  return fn
+    .replace(/^(list|get|search|fetch)_/, "")
+    .replace(/_/g, " ");
+}
+
+interface Pill {
+  key: string;
+  name: string;
+  count: string | null;
+  state: "ok" | "empty" | "error";
+}
+
+const pills = computed<Pill[]>(() =>
+  props.toolCalls.map((tc, i) => {
+    const ok = tc.result?.success !== false && tc.status !== "error";
+    const data = tc.result?.data as Record<string, unknown> | undefined;
+    let count: string | null = null;
+    let state: Pill["state"] = ok ? "ok" : "error";
+    if (ok && data) {
+      const raw =
+        (typeof data.count === "number" && data.count) ||
+        (typeof data.total === "number" && data.total);
+      if (typeof raw === "number") {
+        count = String(raw);
+        if (raw === 0) state = "empty";
+      }
+    }
+    return {
+      key: `${tc.function}-${i}`,
+      name: shortName(tc.function),
+      count,
+      state,
+    };
+  }),
+);
+
+const errorCount = computed(() => pills.value.filter((p) => p.state === "error").length);
+</script>
+
+<template>
+  <div class="briefing-status" :class="{ expanded }">
+    <button class="briefing-status-toggle" @click="expanded = !expanded">
+      <span class="briefing-status-label">Gathered</span>
+      <span
+        v-for="p in pills"
+        :key="p.key"
+        class="briefing-status-pill"
+        :class="`state-${p.state}`"
+      >
+        {{ p.name }}<span v-if="p.count != null" class="pill-count">{{ p.count }}</span>
+      </span>
+      <span v-if="errorCount" class="briefing-status-errcount">{{ errorCount }} failed</span>
+      <span class="briefing-status-chevron" :class="{ open: expanded }">▶</span>
+    </button>
+    <div v-if="expanded" class="briefing-status-detail">
+      <ToolCallCard
+        v-for="(tc, i) in toolCalls"
+        :key="i"
+        :tool-call="tc"
+      />
+    </div>
+  </div>
+</template>
+
+<style scoped>
+.briefing-status {
+  margin: 0.25rem 0 0.5rem;
+  max-width: 80%;
+}
+
+.briefing-status-toggle {
+  display: flex;
+  flex-wrap: wrap;
+  align-items: center;
+  gap: 0.35rem;
+  width: 100%;
+  padding: 0.35rem 0.55rem;
+  background: transparent;
+  border: 1px dashed var(--color-border);
+  border-radius: 8px;
+  cursor: pointer;
+  font: inherit;
+  color: var(--color-text-muted);
+  transition: border-color 0.15s, background 0.15s;
+}
+.briefing-status-toggle:hover {
+  border-color: var(--color-primary);
+  background: color-mix(in srgb, var(--color-primary) 4%, transparent);
+}
+
+.briefing-status-label {
+  font-family: 'Fraunces', Georgia, serif;
+  font-style: italic;
+  font-size: 0.78rem;
+  color: var(--color-primary);
+  opacity: 0.85;
+  margin-right: 0.15rem;
+}
+
+.briefing-status-pill {
+  display: inline-flex;
+  align-items: center;
+  gap: 0.25rem;
+  padding: 0.08rem 0.45rem;
+  font-size: 0.72rem;
+  line-height: 1.45;
+  border-radius: 999px;
+  border: 1px solid var(--color-border);
+  color: var(--color-text-secondary);
+  background: var(--color-bg-card);
+  white-space: nowrap;
+}
+.briefing-status-pill.state-empty {
+  opacity: 0.55;
+}
+.briefing-status-pill.state-error {
+  border-color: color-mix(in srgb, var(--color-danger, #ef4444) 60%, var(--color-border));
+  color: var(--color-danger, #ef4444);
+}
+
+.pill-count {
+  font-weight: 600;
+  font-variant-numeric: tabular-nums;
+  padding: 0 0.25rem;
+  border-radius: 4px;
+  background: color-mix(in srgb, var(--color-primary) 12%, transparent);
+  color: var(--color-text);
+}
+.state-error .pill-count {
+  background: color-mix(in srgb, var(--color-danger, #ef4444) 18%, transparent);
+}
+
+.briefing-status-errcount {
+  font-size: 0.7rem;
+  color: var(--color-danger, #ef4444);
+  font-weight: 600;
+}
+
+.briefing-status-chevron {
+  margin-left: auto;
+  font-size: 0.65rem;
+  opacity: 0.5;
+  transition: transform 0.15s;
+}
+.briefing-status-chevron.open {
+  transform: rotate(90deg);
+}
+
+.briefing-status-detail {
+  margin-top: 0.4rem;
+  padding-left: 0.55rem;
+  display: flex;
+  flex-direction: column;
+  gap: 0.3rem;
+}
+</style>

frontend/src/components/ChatInputBar.vue

@@ -114,6 +114,20 @@ const transcribingVoice = ref(false)
 const recorder = useVoiceRecorder()
 const silenceDetector = useSilenceDetector()
 
+// Live mic halo. A solid red disc sits behind the mic button and scales
+// with `silenceDetector.amplitude` (0..1 RMS, already amplified for
+// visibility). The button itself stays put so the mic icon is always
+// legible on top. 0.2 baseline breathes on silence; loud speech drives
+// the disc out to ~2.6× the button size with a softer radial fade.
+const micGlowStyle = computed(() => {
+  if (!recorder.recording.value) return { display: 'none' }
+  const pulse = 0.2 + Math.min(silenceDetector.amplitude.value, 1) * 0.8
+  return {
+    transform: `translate(-50%, -50%) scale(${1 + pulse * 1.6})`,
+    opacity: String(0.45 + pulse * 0.4),
+  }
+})
+
 async function toggleVoice() {
   if (transcribingVoice.value) return
   if (recorder.recording.value) {
@@ -230,23 +244,25 @@ defineExpose({ focus, prefill })
         class="input-textarea"
       ></textarea>
 
-      <!-- PTT mic -->
-      <button
-        v-if="voiceEnabled"
-        class="btn-icon btn-mic"
-        :class="{ 'mic-recording': recorder.recording.value, 'mic-transcribing': transcribingVoice }"
-        @click.prevent="toggleVoice"
-        :disabled="transcribingVoice || !store.chatReady"
-        :title="recorder.recording.value ? 'Click to stop (or wait for silence)' : 'Click to speak'"
-        :aria-label="recorder.recording.value ? 'Stop recording' : 'Start recording'"
-      >
+      <!-- PTT mic (with live amplitude halo behind) -->
+      <div v-if="voiceEnabled" class="mic-wrapper">
+        <div class="mic-glow" :style="micGlowStyle" aria-hidden="true"></div>
+        <button
+          class="btn-icon btn-mic"
+          :class="{ 'mic-recording': recorder.recording.value, 'mic-transcribing': transcribingVoice }"
+          @click.prevent="toggleVoice"
+          :disabled="transcribingVoice || !store.chatReady"
+          :title="recorder.recording.value ? 'Click to stop (or wait for silence)' : 'Click to speak'"
+          :aria-label="recorder.recording.value ? 'Stop recording' : 'Start recording'"
+        >
         <svg v-if="!transcribingVoice" width="17" height="17" viewBox="0 0 24 24" fill="currentColor">
           <path d="M12 14c1.66 0 3-1.34 3-3V5c0-1.66-1.34-3-3-3S9 3.34 9 5v6c0 1.66 1.34 3 3 3zm-1-9c0-.55.45-1 1-1s1 .45 1 1v6c0 .55-.45 1-1 1s-1-.45-1-1V5zm6 6c0 2.76-2.24 5-5 5s-5-2.24-5-5H5c0 3.53 2.61 6.43 6 6.92V21h2v-3.08c3.39-.49 6-3.39 6-6.92h-2z"/>
         </svg>
         <svg v-else width="17" height="17" viewBox="0 0 24 24" fill="currentColor">
           <circle cx="12" cy="12" r="8" opacity="0.35"/><circle cx="12" cy="12" r="4"/>
         </svg>
-      </button>
+        </button>
+      </div>
 
       <!-- Abort (streaming) or Send -->
       <button
@@ -343,9 +359,50 @@ defineExpose({ focus, prefill })
 .btn-icon:hover { opacity: 1; }
 .btn-icon:disabled { opacity: 0.3; cursor: default; }
 
-.btn-mic.mic-recording { opacity: 1; color: #ef4444; }
+.btn-mic.mic-recording {
+  opacity: 1;
+  /* White icon sits on top of the red halo so it stays legible at any
+     pulse size. */
+  color: #fff;
+  position: relative;
+  z-index: 1;
+}
 .btn-mic.mic-transcribing { opacity: 0.5; }
 
+/* Mic wrapper + live amplitude halo. The glow is a filled red disc
+   absolutely positioned behind the button, scaled/faded by the
+   computed `micGlowStyle` so the user gets unmistakable feedback
+   that their voice is being picked up while the mic icon itself
+   stays put and readable on top. */
+.mic-wrapper {
+  position: relative;
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  flex-shrink: 0;
+}
+.mic-glow {
+  position: absolute;
+  top: 50%;
+  left: 50%;
+  width: 28px;
+  height: 28px;
+  border-radius: 50%;
+  background: radial-gradient(
+    circle,
+    rgba(239, 68, 68, 0.9) 0%,
+    rgba(239, 68, 68, 0.6) 55%,
+    rgba(239, 68, 68, 0) 100%
+  );
+  transform: translate(-50%, -50%) scale(1);
+  transform-origin: center;
+  pointer-events: none;
+  /* Smooth between amplitude samples (~100ms) so the pulse feels continuous
+     rather than stepping. */
+  transition: transform 0.12s ease-out, opacity 0.12s ease-out;
+  z-index: 0;
+}
+
 .note-picker-wrapper { position: relative; }
 .note-picker-dropdown {
   position: absolute;

frontend/src/components/ChatMessage.vue

@@ -3,8 +3,16 @@ import { computed } from "vue";
 import { renderMarkdown } from "@/utils/markdown";
 import { useSettingsStore } from "@/stores/settings";
 import ToolCallCard from "@/components/ToolCallCard.vue";
+import BriefingToolStatusRow from "@/components/BriefingToolStatusRow.vue";
 import type { Message } from "@/types/chat";
 
+const SLOT_LABELS: Record<string, string> = {
+  compilation: "Full Briefing",
+  morning: "Morning Update",
+  midday: "Midday Update",
+  afternoon: "Afternoon Update",
+};
+
 const settingsStore = useSettingsStore();
 
 const props = defineProps<{
@@ -41,6 +49,30 @@ function formatMs(ms: number | null | undefined): string {
   return `${(ms / 1000).toFixed(1)}s`;
 }
 
+const metadata = computed(() => (props.message.metadata ?? {}) as Record<string, unknown>);
+
+const isBriefingIntermediate = computed(
+  () => props.message.role === "assistant" && metadata.value.briefing_intermediate === true,
+);
+
+const briefingSlot = computed(() => {
+  const slot = metadata.value.briefing_slot;
+  return typeof slot === "string" ? slot : null;
+});
+
+const slotLabel = computed(() => {
+  const slot = briefingSlot.value;
+  if (!slot) return null;
+  return SLOT_LABELS[slot] ?? slot;
+});
+
+const isSlotUpdate = computed(
+  () =>
+    !isBriefingIntermediate.value &&
+    briefingSlot.value != null &&
+    briefingSlot.value !== "compilation",
+);
+
 const timingParts = computed((): string[] => {
   const t = props.message.timing;
   if (!t) return [];
@@ -57,11 +89,16 @@ const timingParts = computed((): string[] => {
 </script>
 
 <template>
-  <div class="chat-message" :class="`role-${message.role}`">
+  <!-- Briefing intermediate: compact status row, no bubble -->
+  <div v-if="isBriefingIntermediate" class="chat-message role-assistant briefing-intermediate-row">
+    <BriefingToolStatusRow :tool-calls="message.tool_calls ?? []" />
+  </div>
+  <div v-else class="chat-message" :class="[`role-${message.role}`, { 'slot-update': isSlotUpdate }]">
     <div class="message-group">
-      <div class="message-bubble">
+      <div class="message-bubble" :class="{ 'bubble-slot': isSlotUpdate }">
         <div class="message-header">
           <span class="role-label">{{ roleLabel }}</span>
+          <span v-if="slotLabel" class="slot-badge" :class="{ 'slot-badge-update': isSlotUpdate }">{{ slotLabel }}</span>
           <div class="message-actions" v-if="message.role === 'assistant' && !isStreaming">
             <button class="btn-save" @click="emit('saveAsNote', message.id)" title="Save as note">
               Save as Note
@@ -281,4 +318,39 @@ details[open] .thinking-summary::before {
   margin-right: 0.2rem;
   opacity: 0.5;
 }
+
+/* ── Briefing intermediate row (no bubble) ──────────────── */
+.briefing-intermediate-row {
+  margin-bottom: 0.5rem;
+}
+
+/* ── Slot badge (morning/midday/afternoon/full) ─────────── */
+.slot-badge {
+  font-size: 0.65rem;
+  font-weight: 700;
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
+  padding: 0.1rem 0.45rem;
+  border-radius: 999px;
+  background: color-mix(in srgb, var(--color-primary) 14%, transparent);
+  color: var(--color-primary);
+  border: 1px solid color-mix(in srgb, var(--color-primary) 30%, transparent);
+  margin-left: 0.5rem;
+}
+.slot-badge-update {
+  background: color-mix(in srgb, var(--color-text-muted) 12%, transparent);
+  color: var(--color-text-muted);
+  border-color: var(--color-border);
+}
+
+/* ── Slot update bubbles: softer treatment than compilation ── */
+.slot-update .message-bubble.bubble-slot {
+  background: transparent;
+  border-left: 2px solid var(--color-border);
+  box-shadow: none;
+}
+.slot-update .message-bubble.bubble-slot .message-content {
+  font-size: 0.88rem;
+  color: var(--color-text-secondary);
+}
 </style>

frontend/src/components/ChatPanel.vue

@@ -208,7 +208,7 @@ async function onSubmit(payload: { content: string; contextNoteId?: number }) {
     payload.content,
     payload.contextNoteId,
     includedNoteIds.value.size ? [...includedNoteIds.value] : undefined,
-    true,
+    false,
     undefined,
     excludedNoteIds.value.length ? excludedNoteIds.value : undefined,
     props.projectId ?? store.ragProjectId,
@@ -249,7 +249,7 @@ async function widgetSend(payload: { content: string; contextNoteId?: number })
   widgetConvId.value = conv.id
   emit('conversation-started', conv.id)
 
-  await store.sendMessage(payload.content, payload.contextNoteId, undefined, true)
+  await store.sendMessage(payload.content, payload.contextNoteId, undefined, false)
 
   const msgs = store.currentConversation?.messages ?? []
   const lastAssistant = [...msgs].reverse().find((m: Message) => m.role === 'assistant')
@@ -289,6 +289,23 @@ async function send(text: string) {
   await onSubmit({ content: text })
 }
 
+// ── Briefing slot separator helper ────────────────────────────────────────────
+function hasEarlierBriefingSlot(index: number): boolean {
+  const msgs = store.currentConversation?.messages ?? []
+  for (let i = 0; i < index; i++) {
+    const m = msgs[i]
+    const meta = m?.metadata as Record<string, unknown> | null | undefined
+    if (
+      m?.role === 'assistant' &&
+      meta?.briefing_slot != null &&
+      !meta?.briefing_intermediate
+    ) {
+      return true
+    }
+  }
+  return false
+}
+
 defineExpose({ focus, prefill, send })
 </script>
 
@@ -303,13 +320,11 @@ defineExpose({ focus, prefill, send })
             v-for="(msg, index) in store.currentConversation?.messages ?? []"
             :key="msg.id"
           >
-            <!-- Briefing slot separator: shown before a 2nd+ briefing slot message -->
+            <!-- Briefing slot separator: before any non-first slot message (skip intermediate tool-call rows) -->
             <div
-              v-if="briefingMode && index > 0 && msg.role === 'assistant' && msg.metadata?.rss_item_ids"
+              v-if="briefingMode && msg.role === 'assistant' && msg.metadata?.briefing_slot && !msg.metadata?.briefing_intermediate && hasEarlierBriefingSlot(index)"
               class="briefing-slot-separator"
-            >
-              <span>New Briefing Update</span>
-            </div>
+            ></div>
             <ChatMessage
               :message="msg"
               @save-as-note="handleSaveAsNote"
@@ -537,24 +552,10 @@ defineExpose({ focus, prefill, send })
 
 /* Briefing slot separator */
 .briefing-slot-separator {
-  display: flex;
-  align-items: center;
-  gap: 0.75rem;
-  margin: 1.25rem 0 0.5rem;
-  color: var(--color-text-muted, #888);
-  font-size: 0.7rem;
-  font-weight: 600;
-  letter-spacing: 0.08em;
-  text-transform: uppercase;
-  opacity: 0.7;
-}
-.briefing-slot-separator::before,
-.briefing-slot-separator::after {
-  content: '';
-  flex: 1;
   height: 1px;
+  margin: 1.25rem 0 0.75rem;
   background: var(--color-border, #333);
-  opacity: 0.5;
+  opacity: 0.35;
 }
 
 /* Context sidebar */

frontend/src/components/ToolCallCard.vue

@@ -218,7 +218,7 @@ async function confirmDuplicate() {
   if (confirmState.value !== "idle") return;
   confirmState.value = "creating";
   const args = props.toolCall.arguments as Record<string, unknown>;
-  const isTask = props.toolCall.function === "create_task";
+  const isTask = !!(args.status);
   const endpoint = isTask ? "/api/tasks" : "/api/notes";
   const payload: Record<string, unknown> = {
     title: args.title,

frontend/src/composables/useSilenceDetector.ts

@@ -1,15 +1,54 @@
 import { ref, readonly } from 'vue'
 
 export interface SilenceDetectorOptions {
-  thresholdDb?: number       // default -40
-  silenceDurationMs?: number // default 1500
-  minRecordingMs?: number    // default 500
+  /**
+   * Absolute fallback silence threshold in dBFS, used during the first
+   * moments before any real speech has been observed. Once the session peak
+   * clears `dynamicArmDb` we switch to the dynamic threshold instead.
+   */
+  fallbackThresholdDb?: number // default -45
+  /** How many dB below the session peak counts as "silent" once armed. */
+  dropFromPeakDb?: number // default 15
+  /**
+   * Session peak must reach this level (dBFS) before dynamic thresholding
+   * kicks in. Until then the fallback threshold is used so we don't lock
+   * onto a noise-floor peak.
+   */
+  dynamicArmDb?: number // default -25
+  /** How long to wait at the start of recording before running silence checks. */
+  graceMs?: number // default 1500
+  silenceDurationMs?: number // default 2000
+  minRecordingMs?: number // default 500
 }
 
+/**
+ * Mic silence detector + live amplitude signal.
+ *
+ * Uses `getFloatTimeDomainData()` for honest linear RMS in [-1, 1] space,
+ * then derives dBFS for the silence threshold. The previous implementation
+ * ran RMS over `getByteFrequencyData` bytes — those bytes are already a
+ * dB-scaled quantity, so taking their RMS and re-log'ing it produced
+ * numbers that didn't line up with real dBFS and made any static threshold
+ * unpredictable.
+ *
+ * Silence threshold is dynamic: we track the session peak dBFS and treat
+ * "silent" as "current level is at least [dropFromPeakDb] below peak."
+ * This auto-calibrates to whatever mic and room the user is on. Until the
+ * peak climbs above [dynamicArmDb] we fall back to a conservative static
+ * threshold so a quiet room doesn't spin forever. A grace period at the
+ * start of the recording gives the user time to begin speaking before
+ * silence checks arm.
+ *
+ * `amplitude` is the raw linear RMS scaled ×5 and clamped to 1 so quiet
+ * speech still visibly moves UI that binds to it.
+ */
 export function useSilenceDetector(options: SilenceDetectorOptions = {}) {
   const {
-    thresholdDb = -40,
-    silenceDurationMs = 1500,
+    fallbackThresholdDb = -45,
+    dropFromPeakDb = 15,
+    dynamicArmDb = -25,
+    graceMs = 1500,
+    silenceDurationMs = 2000,
     minRecordingMs = 500,
   } = options
 
@@ -18,31 +57,43 @@ export function useSilenceDetector(options: SilenceDetectorOptions = {}) {
   let intervalId: ReturnType<typeof setInterval> | null = null
   let silenceMs = 0
   let startedAt = 0
+  let peakDb = -100
 
   function start(stream: MediaStream, onSilence: () => void): void {
     stop()
     audioCtx = new AudioContext()
     // Some browsers start AudioContext in "suspended" state — resume so
-    // getByteFrequencyData returns real values instead of all zeros.
+    // the analyser returns real values instead of all zeros.
     audioCtx.resume().catch(() => {})
     const source = audioCtx.createMediaStreamSource(stream)
     const analyser = audioCtx.createAnalyser()
-    analyser.fftSize = 256
+    analyser.fftSize = 1024
     source.connect(analyser)
 
-    const data = new Uint8Array(analyser.frequencyBinCount)
+    const samples = new Float32Array(analyser.fftSize)
     silenceMs = 0
     startedAt = Date.now()
+    peakDb = -100
 
     intervalId = setInterval(() => {
-      analyser.getByteFrequencyData(data)
-      const rms = Math.sqrt(data.reduce((s, v) => s + v * v, 0) / data.length) / 255
-      amplitude.value = rms
+      analyser.getFloatTimeDomainData(samples)
+      let sumSq = 0
+      for (let i = 0; i < samples.length; i++) sumSq += samples[i] * samples[i]
+      const rms = Math.sqrt(sumSq / samples.length)
+      amplitude.value = Math.min(rms * 5, 1)
 
-      const db = rms > 0 ? 20 * Math.log10(rms) : -100
-      if (db < thresholdDb) {
+      const db = rms > 1e-7 ? 20 * Math.log10(rms) : -100
+      if (db > peakDb) peakDb = db
+
+      const elapsed = Date.now() - startedAt
+      if (elapsed < graceMs) return
+
+      const threshold =
+        peakDb > dynamicArmDb ? peakDb - dropFromPeakDb : fallbackThresholdDb
+
+      if (db < threshold) {
         silenceMs += 100
-        if (silenceMs >= silenceDurationMs && Date.now() - startedAt >= minRecordingMs) {
+        if (silenceMs >= silenceDurationMs && elapsed >= minRecordingMs) {
           stop()
           onSilence()
         }
@@ -63,6 +114,7 @@ export function useSilenceDetector(options: SilenceDetectorOptions = {}) {
     }
     amplitude.value = 0
     silenceMs = 0
+    peakDb = -100
   }
 
   return { amplitude: readonly(amplitude), start, stop }

frontend/src/views/ChatView.vue

@@ -639,6 +639,9 @@ onUnmounted(() => {
   gap: 1rem;
   color: var(--color-text-muted);
 }
+.no-conversation .btn-new-conv {
+  flex: none;
+}
 
 .empty-msg {
   color: var(--color-text-muted);

frontend/src/views/KnowledgeView.vue

@@ -247,7 +247,7 @@ const chatCollapsed = ref(false);
 const chatConvId = ref<number | null>(null);
 async function onMinichatSubmit(payload: { content: string; contextNoteId?: number }) {
   if (!chatConvId.value) {
-    const conv = await chatStore.createConversation("Knowledge chat");
+    const conv = await chatStore.createConversation();
     chatConvId.value = conv.id;
     await chatStore.fetchConversation(conv.id);
   } else if (chatStore.currentConversation?.id !== chatConvId.value) {
@@ -255,7 +255,7 @@ async function onMinichatSubmit(payload: { content: string; contextNoteId?: numb
   }
   chatOpen.value = true;
   chatCollapsed.value = false;
-  await chatStore.sendMessage(payload.content, payload.contextNoteId, undefined, true);
+  await chatStore.sendMessage(payload.content, payload.contextNoteId, undefined, false);
 }
 
 // ─── Auto-refresh cards when chat creates/edits notes or tasks ───────────────
@@ -539,6 +539,7 @@ onUnmounted(() => {
               <span v-if="item.note_type === 'note'">Note</span>
               <span v-else-if="item.note_type === 'person'">Person</span>
               <span v-else-if="item.note_type === 'place'">Place</span>
+              <span v-else-if="item.note_type === 'task'">Task</span>
               <span v-else>List</span>
             </span>
 
@@ -1025,8 +1026,8 @@ onUnmounted(() => {
   background: linear-gradient(90deg, #7c3aed, #a78bfa);
 }
 .k-card--task::before {
-  width: 50%;
-  background: linear-gradient(90deg, #d4a017, transparent);
+  right: 0;
+  background: linear-gradient(90deg, #d4a017, #fbbf24);
 }
 .k-card--list::before {
   right: 0;
@@ -1064,7 +1065,7 @@ onUnmounted(() => {
 .badge--person { background: rgba(16,185,129,0.15); color: #34d399; }
 .badge--place  { background: rgba(245,158,11,0.15); color: #fbbf24; }
 .badge--list   { background: rgba(56,189,248,0.15); color: #7dd3fc; }
-.badge--task   { background: rgba(167,139,250,0.15); color: #a78bfa; }
+.badge--task   { background: rgba(212,160,23,0.15); color: #fbbf24; }
 
 .k-card-body { flex: 1; padding-right: 40px; }
 .k-card-title {

frontend/src/views/SettingsView.vue

@@ -1,5 +1,5 @@
 <script setup lang="ts">
-import { ref, watch, onMounted } from "vue";
+import { ref, computed, watch, onMounted } from "vue";
 import { useSettingsStore } from "@/stores/settings";
 import { useAuthStore } from "@/stores/auth";
 import { useToastStore } from "@/stores/toast";
@@ -94,17 +94,54 @@ const mcpInfo = ref<{ available: boolean; filename: string | null } | null>(null
 const mcpInfoLoading = ref(false);
 
 const origin = window.location.origin;
-const mcpConfigSnippet = JSON.stringify({
+const mcpClientTab = ref<'claude-code' | 'claude-desktop' | 'other'>('claude-code');
+const copiedSnippetKey = ref<string | null>(null);
+
+const effectiveApiKey = computed(() => newKeyValue.value || '<your-api-key>');
+
+const claudeCodeCommand = computed(() => {
+  return `claude mcp add --transport stdio --scope user fable \\
+  --env FABLE_URL=${origin} \\
+  --env FABLE_API_KEY=${effectiveApiKey.value} \\
+  -- fable-mcp`;
+});
+
+const mcpConfigSnippet = computed(() => JSON.stringify({
   mcpServers: {
     fable: {
       command: "fable-mcp",
       env: {
-        FABLE_URL: window.location.origin,
-        FABLE_API_KEY: "<your-api-key>",
+        FABLE_URL: origin,
+        FABLE_API_KEY: effectiveApiKey.value,
       },
     },
   },
-}, null, 2);
+}, null, 2));
+
+async function copyToClipboard(text: string) {
+  try {
+    await navigator.clipboard.writeText(text);
+  } catch {
+    // Fallback for http (non-secure) contexts where clipboard API is unavailable
+    const ta = document.createElement('textarea');
+    ta.value = text;
+    ta.style.position = 'fixed';
+    ta.style.opacity = '0';
+    document.body.appendChild(ta);
+    ta.focus();
+    ta.select();
+    document.execCommand('copy');
+    document.body.removeChild(ta);
+  }
+}
+
+async function copySnippet(text: string, key: string) {
+  await copyToClipboard(text);
+  copiedSnippetKey.value = key;
+  setTimeout(() => {
+    if (copiedSnippetKey.value === key) copiedSnippetKey.value = null;
+  }, 2000);
+}
 
 async function loadMcpInfo() {
   if (mcpInfo.value !== null) return;
@@ -142,20 +179,7 @@ async function revokeApiKey(id: number) {
 }
 
 async function copyApiKey() {
-  try {
-    await navigator.clipboard.writeText(newKeyValue.value);
-  } catch {
-    // Fallback for http (non-secure) contexts where clipboard API is unavailable
-    const ta = document.createElement('textarea');
-    ta.value = newKeyValue.value;
-    ta.style.position = 'fixed';
-    ta.style.opacity = '0';
-    document.body.appendChild(ta);
-    ta.focus();
-    ta.select();
-    document.execCommand('copy');
-    document.body.removeChild(ta);
-  }
+  await copyToClipboard(newKeyValue.value);
   apiKeyCopied.value = true;
   setTimeout(() => { apiKeyCopied.value = false; }, 2000);
 }
@@ -2529,21 +2553,113 @@ function formatUserDate(iso: string): string {
 
             <div class="mcp-install-steps">
               <h3>Installation</h3>
-              <ol>
+
+              <div class="mcp-client-tabs" role="tablist">
+                <button
+                  type="button"
+                  role="tab"
+                  :aria-selected="mcpClientTab === 'claude-code'"
+                  :class="['mcp-client-tab', { active: mcpClientTab === 'claude-code' }]"
+                  @click="mcpClientTab = 'claude-code'"
+                >Claude Code</button>
+                <button
+                  type="button"
+                  role="tab"
+                  :aria-selected="mcpClientTab === 'claude-desktop'"
+                  :class="['mcp-client-tab', { active: mcpClientTab === 'claude-desktop' }]"
+                  @click="mcpClientTab = 'claude-desktop'"
+                >Claude Desktop</button>
+                <button
+                  type="button"
+                  role="tab"
+                  :aria-selected="mcpClientTab === 'other'"
+                  :class="['mcp-client-tab', { active: mcpClientTab === 'other' }]"
+                  @click="mcpClientTab = 'other'"
+                >Other</button>
+              </div>
+
+              <!-- Claude Code tab -->
+              <ol v-if="mcpClientTab === 'claude-code'">
                 <li>
-                  Download the wheel above and install it:
-                  <pre class="mcp-code">pip install {{ mcpInfo.filename }}</pre>
+                  Download the wheel above and install it with <a href="https://docs.astral.sh/uv/" target="_blank" rel="noopener">uv</a> or <a href="https://pipx.pypa.io/" target="_blank" rel="noopener">pipx</a> — either one works:
+                  <div class="mcp-code-row">
+                    <pre class="mcp-code">uv tool install ./{{ mcpInfo.filename }}</pre>
+                    <button class="btn btn-secondary btn-sm" @click="copySnippet(`uv tool install ./${mcpInfo.filename}`, 'cc-install-uv')">
+                      {{ copiedSnippetKey === 'cc-install-uv' ? 'Copied' : 'Copy' }}
+                    </button>
+                  </div>
+                  <div class="mcp-code-row">
+                    <pre class="mcp-code">pipx install ./{{ mcpInfo.filename }}</pre>
+                    <button class="btn btn-secondary btn-sm" @click="copySnippet(`pipx install ./${mcpInfo.filename}`, 'cc-install-pipx')">
+                      {{ copiedSnippetKey === 'cc-install-pipx' ? 'Copied' : 'Copy' }}
+                    </button>
+                  </div>
+                  <p class="mcp-hint">This puts <code>fable-mcp</code> on your PATH so Claude Code can launch it.</p>
                 </li>
                 <li>
-                  Create a <code>.env</code> file (or set environment variables):
-                  <pre class="mcp-code">FABLE_URL={{ origin }}
-FABLE_API_KEY=&lt;your-api-key&gt;</pre>
+                  Register the server with Claude Code:
+                  <div class="mcp-code-row">
+                    <pre class="mcp-code">{{ claudeCodeCommand }}</pre>
+                    <button class="btn btn-secondary btn-sm" @click="copySnippet(claudeCodeCommand, 'cc-add')">
+                      {{ copiedSnippetKey === 'cc-add' ? 'Copied' : 'Copy' }}
+                    </button>
+                  </div>
+                  <p class="mcp-hint">
+                    <code>--scope user</code> makes <code>fable</code> available across all your projects. Use <code>--scope project</code> to write it into the current repo's <code>.mcp.json</code> instead, or <code>--scope local</code> for this machine and repo only.
+                    <span v-if="!newKeyValue"> Generate an API key above to pre-fill the command.</span>
+                  </p>
                 </li>
                 <li>
-                  Add to your Claude MCP config (<code>~/.claude.json</code> or the Claude Desktop config):
-                  <pre class="mcp-code">{{ mcpConfigSnippet }}</pre>
+                  Verify the connection by running <code>/mcp</code> inside Claude Code — <code>fable</code> should appear as connected.
                 </li>
               </ol>
+
+              <!-- Claude Desktop tab -->
+              <ol v-else-if="mcpClientTab === 'claude-desktop'">
+                <li>
+                  Download the wheel above and install it with uv or pipx:
+                  <div class="mcp-code-row">
+                    <pre class="mcp-code">uv tool install ./{{ mcpInfo.filename }}</pre>
+                    <button class="btn btn-secondary btn-sm" @click="copySnippet(`uv tool install ./${mcpInfo.filename}`, 'cd-install')">
+                      {{ copiedSnippetKey === 'cd-install' ? 'Copied' : 'Copy' }}
+                    </button>
+                  </div>
+                </li>
+                <li>
+                  Add this block to your Claude Desktop MCP config file (<code>~/Library/Application Support/Claude/claude_desktop_config.json</code> on macOS, <code>%APPDATA%\Claude\claude_desktop_config.json</code> on Windows):
+                  <div class="mcp-code-row">
+                    <pre class="mcp-code">{{ mcpConfigSnippet }}</pre>
+                    <button class="btn btn-secondary btn-sm" @click="copySnippet(mcpConfigSnippet, 'cd-config')">
+                      {{ copiedSnippetKey === 'cd-config' ? 'Copied' : 'Copy' }}
+                    </button>
+                  </div>
+                </li>
+                <li>
+                  Restart Claude Desktop. The Fable tools should appear in the available tools list.
+                </li>
+              </ol>
+
+              <!-- Other tab -->
+              <div v-else class="mcp-other">
+                <p>
+                  Any MCP-compatible client can launch <code>fable-mcp</code> over stdio. The exact setup depends on the client, but in general you'll need:
+                </p>
+                <ul class="mcp-plain-list">
+                  <li>Install the wheel: <code>uv tool install ./{{ mcpInfo.filename }}</code> or <code>pipx install ./{{ mcpInfo.filename }}</code></li>
+                  <li>Command: <code>fable-mcp</code></li>
+                  <li>Transport: <code>stdio</code></li>
+                  <li>Environment variables:
+                    <div class="mcp-code-row">
+                      <pre class="mcp-code">FABLE_URL={{ origin }}
+FABLE_API_KEY={{ effectiveApiKey }}</pre>
+                      <button class="btn btn-secondary btn-sm" @click="copySnippet(`FABLE_URL=${origin}\nFABLE_API_KEY=${effectiveApiKey}`, 'other-env')">
+                        {{ copiedSnippetKey === 'other-env' ? 'Copied' : 'Copy' }}
+                      </button>
+                    </div>
+                  </li>
+                </ul>
+                <p class="mcp-hint">Consult your MCP client's documentation for how to register a stdio server. These instructions have only been tested with Claude Code.</p>
+              </div>
             </div>
           </div>
           <div v-else class="mcp-unavailable">
@@ -4215,6 +4331,54 @@ FABLE_API_KEY=&lt;your-api-key&gt;</pre>
   white-space: pre-wrap;
   word-break: break-all;
   overflow-x: auto;
+  flex: 1;
+}
+.mcp-client-tabs {
+  display: flex;
+  gap: 0.25rem;
+  margin-bottom: 1rem;
+  border-bottom: 1px solid var(--color-border);
+}
+.mcp-client-tab {
+  background: transparent;
+  border: none;
+  padding: 0.5rem 0.9rem;
+  font-size: 0.85rem;
+  color: var(--color-text-muted);
+  cursor: pointer;
+  border-bottom: 2px solid transparent;
+  margin-bottom: -1px;
+  transition: color 0.15s, border-color 0.15s;
+}
+.mcp-client-tab:hover { color: var(--color-text); }
+.mcp-client-tab.active {
+  color: var(--color-primary);
+  border-bottom-color: var(--color-primary);
+  font-weight: 600;
+}
+.mcp-code-row {
+  display: flex;
+  align-items: stretch;
+  gap: 0.4rem;
+  margin-top: 0.4rem;
+}
+.mcp-code-row .mcp-code { margin-top: 0; }
+.mcp-code-row .btn-sm { white-space: nowrap; }
+.mcp-hint {
+  margin-top: 0.5rem;
+  font-size: 0.8rem;
+  opacity: 0.7;
+  line-height: 1.5;
+}
+.mcp-other p { font-size: 0.9rem; line-height: 1.6; }
+.mcp-plain-list {
+  list-style: disc;
+  padding-left: 1.25rem;
+  display: flex;
+  flex-direction: column;
+  gap: 0.5rem;
+  font-size: 0.9rem;
+  line-height: 1.6;
 }
 
 /* Voice tab */

frontend/src/views/WorkspaceView.vue

@@ -75,8 +75,11 @@ watch(
         noteEditorRef.value?.reload();
       }
       if (
-        ["create_task", "update_task", "create_milestone", "update_milestone"].includes(tc.function) &&
-        tc.status === "success"
+        tc.status === "success" && (
+          ["create_milestone", "update_milestone"].includes(tc.function) ||
+          (tc.function === "create_note" && tc.result?.data?.status) ||
+          (tc.function === "update_note" && tc.result?.data?.status !== undefined)
+        )
       ) {
         taskPanelRef.value?.reload();
       }
@@ -135,8 +138,7 @@ onMounted(async () => {
 
   if (workspaceConvId === null) {
     // Create a new workspace conversation and persist its ID
-    const title = project.value ? `${project.value.title} — Workspace` : "Workspace";
-    const conv = await chatStore.createConversation(title);
+    const conv = await chatStore.createConversation();
     workspaceConvId = conv.id;
     isNewConv = true;
     await chatStore.fetchConversation(conv.id);

infra/Dockerfile.runner-base

@@ -1,26 +1,28 @@
 # Runner base image for Forgejo act_runner job containers.
-# Pre-installs Python 3.12 and Node 20 so the test job skips the
-# ~3-4 minute deadsnakes PPA install on every run.
+# Pre-installs Python 3.12, Node 22, uv, and ruff so workflows skip
+# lengthy runtime installs on every run.
 #
-# Build and push (one-time setup, re-run if this file changes):
+# Build and push (re-run when this file changes):
 #   docker build -f infra/Dockerfile.runner-base \
-#     -t git.fabledsword.com/bvandeusen/runner-base:py3.12-node22 .
-#   docker push git.fabledsword.com/bvandeusen/runner-base:py3.12-node22
+#     -t git.fabledsword.com/bvandeusen/runner-base:ci-runner .
+#   docker push git.fabledsword.com/bvandeusen/runner-base:ci-runner
 #
-# Then redeploy the act_runner stack in Portainer to pick up the new label.
+# Then redeploy the act_runner stack in Portainer to pick up the new image.
 
 FROM ubuntu:24.04
 
 ENV DEBIAN_FRONTEND=noninteractive
+ENV TZ=UTC
 
-# Split into separate RUN steps so each pushed layer is smaller.
-# One combined layer would be ~280MB and exceeds the nginx upload timeout.
+# Split into separate RUN steps so each pushed layer is smaller — one
+# combined layer exceeds the nginx upload timeout on the self-hosted
+# Forgejo registry.
 
-# Python 3.12 ships in Ubuntu 24.04 main repos — no PPA needed.
+# Python 3.12 (ships in Ubuntu 24.04 main repos) + core tools
 RUN apt-get update -qq && \
     apt-get install -y -qq \
-      python3.12 python3.12-dev python3.12-venv python3-pip \
-      git curl ca-certificates gnupg && \
+      python3.12 python3.12-dev python3.12-venv \
+      git curl ca-certificates gnupg jq tzdata && \
     apt-get clean && rm -rf /var/lib/apt/lists/*
 
 # Node 22 LTS via NodeSource
@@ -33,3 +35,14 @@ RUN curl -fsSL https://deb.nodesource.com/setup_22.x | bash - && \
 RUN apt-get update -qq && \
     apt-get install -y -qq docker.io && \
     apt-get clean && rm -rf /var/lib/apt/lists/*
+
+# uv — fast Python package installer/resolver (~10x faster than pip).
+# Used by test jobs instead of pip for venv creation + dep installs.
+RUN curl -LsSf https://astral.sh/uv/install.sh | env INSTALLER_NO_MODIFY_PATH=1 sh && \
+    mv /root/.local/bin/uv /usr/local/bin/ && \
+    mv /root/.local/bin/uvx /usr/local/bin/
+
+# ruff — Python linter. Baked in so the lint job is just
+# `ruff check src/` with zero install overhead.
+RUN curl -LsSf https://astral.sh/ruff/install.sh | env INSTALLER_NO_MODIFY_PATH=1 sh && \
+    mv /root/.local/bin/ruff /usr/local/bin/

src/fabledassistant/app.py

@@ -169,11 +169,12 @@ def create_app() -> Quart:
 
         async def _warm_model(model: str) -> None:
             """Warm an already-installed model into VRAM (no pull)."""
+            from fabledassistant.services.llm import keep_alive_for
             try:
                 async with httpx.AsyncClient(timeout=300.0) as client:
                     await client.post(
                         f"{Config.OLLAMA_URL}/api/generate",
-                        json={"model": model, "prompt": "", "keep_alive": "2h"},
+                        json={"model": model, "prompt": "", "keep_alive": keep_alive_for(model)},
                     )
                 logger.info("Warmed model '%s' into VRAM", model)
             except Exception:
@@ -187,14 +188,17 @@ def create_app() -> Quart:
             The num_ctx must match what real requests will use so Ollama doesn't reload.
             """
             try:
-                from fabledassistant.services.llm import build_context, pick_num_ctx
+                from fabledassistant.services.llm import build_context, keep_alive_for, pick_num_ctx
+                from fabledassistant.services.tools import get_tools_for_user
                 messages, _ = await build_context(
                     user_id=user_id,
                     history=[],
                     current_note_id=None,
                     user_message=" ",
                 )
-                num_ctx = pick_num_ctx(messages)
+                # Include tool schemas so num_ctx matches real chat requests.
+                tools = await get_tools_for_user(user_id)
+                num_ctx = pick_num_ctx(messages, tools=tools)
                 async with httpx.AsyncClient(timeout=120.0) as client:
                     await client.post(
                         f"{Config.OLLAMA_URL}/api/chat",
@@ -203,7 +207,7 @@ def create_app() -> Quart:
                             "messages": messages,
                             "stream": False,
                             "options": {"num_predict": 1, "num_ctx": num_ctx},
-                            "keep_alive": "2h",
+                            "keep_alive": keep_alive_for(model),
                         },
                     )
                 logger.info("Primed KV cache for user %d with model '%s' (num_ctx=%d)", user_id, model, num_ctx)

src/fabledassistant/config.py

@@ -27,7 +27,13 @@ class Config:
     # Lightweight model for background tasks (title generation, tag suggestions,
     # project summaries, RSS classification). Using a separate model keeps the
     # main model's KV cache intact between user messages, enabling prefix cache hits.
-    OLLAMA_BACKGROUND_MODEL: str = os.environ.get("OLLAMA_BACKGROUND_MODEL", "qwen2.5:3b")
+    OLLAMA_BACKGROUND_MODEL: str = os.environ.get("OLLAMA_BACKGROUND_MODEL", "gemma3:4b")
+    # Ollama keep_alive — how long a model stays resident in VRAM after its last
+    # request. Main model gets a longer window since it's used interactively;
+    # the background model is called sporadically and doesn't need to camp VRAM.
+    # Format matches Ollama's duration strings: "30m", "10m", "1h", "0s", "-1" (forever).
+    OLLAMA_KEEP_ALIVE_MAIN: str = os.environ.get("OLLAMA_KEEP_ALIVE_MAIN", "30m")
+    OLLAMA_KEEP_ALIVE_BACKGROUND: str = os.environ.get("OLLAMA_KEEP_ALIVE_BACKGROUND", "10m")
     # KV cache context window for generation. Keep this as small as practical —
     # a larger context forces more KV cache into CPU RAM, drastically slowing prefill.
     # 16384 covers ~30+ message conversations with our system prompt comfortably.

src/fabledassistant/models/rss_feed.py

@@ -1,6 +1,6 @@
 from datetime import datetime, timezone
 
-from sqlalchemy import ARRAY, DateTime, ForeignKey, Index, Integer, Text, UniqueConstraint
+from sqlalchemy import ARRAY, BigInteger, DateTime, ForeignKey, Index, Integer, Text, UniqueConstraint
 from sqlalchemy.orm import Mapped, mapped_column, relationship
 
 from fabledassistant.models import Base
@@ -46,6 +46,17 @@ class RssItem(Base):
     published_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
     # Truncated to 2000 chars to keep DB size reasonable
     content: Mapped[str] = mapped_column(Text, default="")
+    # Full trafilatura-extracted article body, populated lazily on first
+    # discuss-click / enrichment pass. Nullable — most items never get this
+    # cached. Expires naturally with the item (90-day retention).
+    content_full: Mapped[str | None] = mapped_column(Text, nullable=True)
+    # Map-reduced conversation-ready context derived from content_full. See
+    # services/article_context.py — populated on first discuss click so
+    # repeat clicks skip both the fetch and the LLM map step.
+    context_prepared: Mapped[str | None] = mapped_column(Text, nullable=True)
+    content_fetched_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True
+    )
     fetched_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), default=lambda: datetime.now(timezone.utc)
     )
@@ -55,6 +66,13 @@ class RssItem(Base):
     classified_at: Mapped[datetime | None] = mapped_column(
         DateTime(timezone=True), nullable=True
     )
+    # Note persisting the first-click discussion summary. Set by the article
+    # discussion pipeline once the seeded chat completes its first assistant
+    # reply; links back into RAG so re-discussing the same article lands the
+    # prior summary in context.
+    discussion_note_id: Mapped[int | None] = mapped_column(
+        BigInteger, ForeignKey("notes.id", ondelete="SET NULL"), nullable=True
+    )
 
     feed: Mapped["RssFeed"] = relationship(back_populates="items")
 

src/fabledassistant/routes/briefing.py

@@ -308,14 +308,55 @@ async def manual_trigger():
         logger.warning("Pre-trigger refresh failed for user %d", g.user.id, exc_info=True)
 
     from fabledassistant.services.briefing_pipeline import run_compilation
+    from fabledassistant.services.briefing_scheduler import _persist_agentic_messages
 
     model = await get_setting(g.user.id, "default_model", "")
     conv = await get_or_create_today_conversation(g.user.id, model)
     text, metadata = await run_compilation(g.user.id, slot, model)
-    msg = await post_message(conv.id, "assistant", text, metadata=metadata)
+    await _persist_agentic_messages(conv.id, slot, metadata)
+    final_meta = {k: v for k, v in metadata.items() if k != "agentic_messages"}
+    final_meta["briefing_slot"] = slot
+    msg = await post_message(conv.id, "assistant", text, metadata=final_meta)
     return jsonify({"conversation_id": conv.id, "message_id": msg.id, "slot": slot})
 
 
+@briefing_bp.route("/reset-today", methods=["POST"])
+@_REQUIRE
+async def reset_today_briefing():
+    """Delete all messages in today's briefing conversation.
+
+    The conversation row itself is kept so its id stays stable for any
+    open UI sessions. Intended for "wipe and start fresh" workflows
+    driven from the MCP when iterating on prompts. Pair with
+    ``POST /api/briefing/trigger`` to immediately regenerate.
+    """
+    from sqlalchemy import delete as _delete
+
+    from fabledassistant.services.tz import user_briefing_date
+
+    # User-local briefing day (flips at 4am local), not ``date.today()`` —
+    # see services/tz.py for rationale.
+    today = await user_briefing_date(g.user.id)
+    async with async_session() as session:
+        result = await session.execute(
+            select(Conversation).where(
+                Conversation.user_id == g.user.id,
+                Conversation.conversation_type == "briefing",
+                Conversation.briefing_date == today,
+            )
+        )
+        conv = result.scalars().first()
+        if conv is None:
+            return jsonify({"deleted": 0, "conversation_id": None})
+        deleted_result = await session.execute(
+            _delete(Message).where(Message.conversation_id == conv.id)
+        )
+        await session.commit()
+        deleted = deleted_result.rowcount or 0
+
+    return jsonify({"deleted": deleted, "conversation_id": conv.id})
+
+
 # ── RSS Reactions ──────────────────────────────────────────────────────────────
 
 @briefing_bp.route("/rss-reactions", methods=["POST"])
@@ -491,25 +532,21 @@ async def discuss_article(item_id: int):
     if get_buffer(conv_id) is not None:
         return jsonify({"error": "Generation already in progress"}), 409
 
-    from fabledassistant.services.rss import _fetch_full_article
-    article_content = await _fetch_full_article(item.url) or item.content or ""
+    # Shared helper handles the three-layer cache (context_prepared →
+    # content_full → fresh fetch), writes the synthetic read_article tool
+    # exchange and the conversational seed user prompt into the conversation.
+    # The /news from-article route calls the same helper so behavior stays
+    # byte-identical across entry points.
+    from fabledassistant.services.article_context import (
+        EmptyArticleError,
+        seed_article_discussion,
+    )
 
-    # Store synthetic assistant message with read_article tool result
-    synthetic_tool_calls = [{
-        "function": "read_article",
-        "arguments": {"url": item.url},
-        "result": {
-            "success": True,
-            "type": "article_content",
-            "url": item.url,
-            "content": article_content,
-            "truncated": False,
-        },
-    }]
-    await add_message(conv_id, "assistant", "", status="complete", tool_calls=synthetic_tool_calls)
-
-    # Store user message
-    await add_message(conv_id, "user", "Please summarize and discuss this article.")
+    model = await get_setting(uid, "default_model", "") or ""
+    try:
+        discuss_prompt = await seed_article_discussion(conv_id, item, model)
+    except EmptyArticleError as e:
+        return jsonify({"error": str(e)}), 422
 
     # Reload conversation with fresh messages to build history
     conv = await get_conversation(uid, conv_id)
@@ -531,16 +568,13 @@ async def discuss_article(item_id: int):
         else:
             history.append(msg_dict)
 
-    model = await get_setting(uid, "default_model", "") or ""
-
     assistant_msg = await add_message(conv_id, "assistant", "", status="generating")
     buf = create_buffer(conv_id, assistant_msg.id)
 
     asyncio.create_task(run_generation(
         buf, history, model,
         uid, conv_id, conv.title or "",
-        "Please summarize and discuss this article.",
-        think=True,
+        discuss_prompt,
     ))
 
     return jsonify({"assistant_message_id": assistant_msg.id, "status": "generating"}), 202
@@ -639,7 +673,6 @@ async def discuss_topic(topic: str):
         buf, history, model,
         uid, conv_id, conv.title or "",
         user_prompt,
-        think=True,
     ))
 
     return jsonify({

src/fabledassistant/routes/chat.py

@@ -350,11 +350,12 @@ async def warm_model_route():
         return jsonify({"error": "model is required"}), 400
 
     async def _warm():
+        from fabledassistant.services.llm import keep_alive_for
         try:
             async with httpx.AsyncClient(timeout=300.0) as client:
                 await client.post(
                     f"{Config.OLLAMA_URL}/api/generate",
-                    json={"model": model, "prompt": "", "keep_alive": "30m"},
+                    json={"model": model, "prompt": "", "keep_alive": keep_alive_for(model)},
                 )
             logger.info("Warmed model %s", model)
         except Exception as e:
@@ -509,47 +510,78 @@ async def delete_model_route():
 @chat_bp.route("/from-article/<int:item_id>", methods=["POST"])
 @login_required
 async def create_conversation_from_article(item_id: int):
-    """Create a chat conversation seeded with an RSS article's content."""
+    """Create a chat conversation seeded for article discussion and auto-run.
+
+    Mirrors the briefing ``discuss_article`` route: creates a fresh
+    conversation, stages the shared synthetic read_article exchange + seed
+    prompt, then kicks off generation so the client lands on an in-flight
+    stream. The Flutter and web chat screens reconnect to the running buffer
+    on mount.
+    """
     from sqlalchemy import select as _select
     from fabledassistant.models import async_session as _async_session
-    from fabledassistant.models.conversation import Message
     from fabledassistant.models.rss_feed import RssItem, RssFeed
+    from fabledassistant.services.article_context import (
+        EmptyArticleError,
+        seed_article_discussion,
+    )
 
     uid = get_current_user_id()
 
     async with _async_session() as session:
         result = await session.execute(
-            _select(RssItem, RssFeed.title.label("feed_title"))
+            _select(RssItem)
             .join(RssFeed, RssItem.feed_id == RssFeed.id)
             .where(RssItem.id == item_id, RssFeed.user_id == uid)
         )
-        row = result.first()
+        item = result.scalars().first()
 
-    if row is None:
+    if item is None:
         return jsonify({"error": "Article not found"}), 404
 
-    item, feed_title = row
-
     conv_title = (item.title or "Article discussion")[:80]
     conv = await create_conversation(uid, title=conv_title, conversation_type="chat")
 
-    from fabledassistant.services.rss import _fetch_full_article
-    source = feed_title or "News"
-    content_body = (await _fetch_full_article(item.url) if item.url else None) or (item.content or "").strip()
-    seeded_text = f"**{source}**\n\n**{item.title}**"
-    if content_body:
-        seeded_text += f"\n\n{content_body}"
-    if item.url:
-        seeded_text += f"\n\nSource: {item.url}"
+    model = await get_setting(uid, "default_model", "") or Config.OLLAMA_MODEL
+    try:
+        discuss_prompt = await seed_article_discussion(conv.id, item, model)
+    except EmptyArticleError as e:
+        # Roll back the empty conversation so the user doesn't end up with a
+        # phantom entry in their chat list.
+        try:
+            await delete_conversation(uid, conv.id)
+        except Exception:
+            logger.warning("Failed to clean up empty article conversation %s", conv.id)
+        return jsonify({"error": str(e)}), 422
 
-    async with _async_session() as session:
-        msg = Message(
-            conversation_id=conv.id,
-            role="assistant",
-            content=seeded_text,
-            msg_metadata={"rss_item_ids": [item_id]},
-        )
-        session.add(msg)
-        await session.commit()
+    # Reload conversation so we see the two messages the helper just added.
+    conv = await get_conversation(uid, conv.id)
+    assert conv is not None
 
-    return jsonify({"conversation_id": conv.id}), 201
+    history: list[dict] = []
+    for msg in conv.messages:
+        if msg.role == "system":
+            continue
+        msg_dict: dict = {"role": msg.role, "content": msg.content or ""}
+        if msg.tool_calls:
+            msg_dict["tool_calls"] = [
+                {"function": {"name": tc["function"], "arguments": tc["arguments"]}}
+                for tc in msg.tool_calls
+            ]
+            history.append(msg_dict)
+            for tc in msg.tool_calls:
+                history.append({"role": "tool", "content": json.dumps(tc.get("result", {}))})
+        else:
+            history.append(msg_dict)
+
+    assistant_msg = await add_message(conv.id, "assistant", "", status="generating")
+    buf = create_buffer(conv.id, assistant_msg.id)
+    asyncio.create_task(run_generation(
+        buf, history, model, uid, conv.id, conv.title or "", discuss_prompt,
+    ))
+
+    return jsonify({
+        "conversation_id": conv.id,
+        "assistant_message_id": assistant_msg.id,
+        "status": "generating",
+    }), 202

src/fabledassistant/routes/quick_capture.py

@@ -11,7 +11,6 @@ from quart import Blueprint, jsonify, request
 
 from fabledassistant.auth import get_current_user_id, login_required
 from fabledassistant.config import Config
-from fabledassistant.services.generation_task import _should_think
 from fabledassistant.services.llm import stream_chat_with_tools
 from fabledassistant.services.tools import execute_tool, get_tools_for_user
 
@@ -21,7 +20,7 @@ quick_capture_bp = Blueprint("quick_capture", __name__, url_prefix="/api/quick-c
 
 # Tools offered to the quick-capture endpoint. Excludes destructive ops,
 # read-only queries, and conversational-only tools.
-_CAPTURE_TOOL_NAMES = {"create_note", "create_task", "create_event", "update_note", "research_topic"}
+_CAPTURE_TOOL_NAMES = {"create_note", "create_event", "update_note", "research_topic"}
 
 _SYSTEM_PROMPT = """\
 Today is {today}. You are a quick-capture assistant. The user has sent a short \
@@ -53,11 +52,10 @@ async def quick_capture_route():
         {"role": "user", "content": text},
     ]
 
-    think = _should_think(text, think_requested=True)
-
+    # Quick capture is a fast classification path — never think.
     tool_calls: list[dict] = []
     try:
-        async for chunk in stream_chat_with_tools(messages, model, tools=capture_tools, think=think, num_ctx=4096):
+        async for chunk in stream_chat_with_tools(messages, model, tools=capture_tools, think=False, num_ctx=4096):
             if chunk.type == "tool_calls" and chunk.tool_calls:
                 tool_calls = chunk.tool_calls
     except Exception:

src/fabledassistant/routes/settings.py

@@ -16,6 +16,7 @@ async def _prime_kv_cache_bg(user_id: int, model: str) -> None:
     """Fire-and-forget: prime Ollama's KV cache with the user's system prompt."""
     import httpx
     from fabledassistant.services.llm import build_context, pick_num_ctx
+    from fabledassistant.services.tools import get_tools_for_user
     try:
         messages, _ = await build_context(
             user_id=user_id,
@@ -23,7 +24,12 @@ async def _prime_kv_cache_bg(user_id: int, model: str) -> None:
             current_note_id=None,
             user_message=" ",
         )
-        num_ctx = pick_num_ctx(messages)
+        # Size the prime to match what real chat requests will use, including
+        # tool schemas — otherwise Ollama reloads the model on the first real
+        # request and throws away the cache we just built.
+        tools = await get_tools_for_user(user_id)
+        num_ctx = pick_num_ctx(messages, tools=tools)
+        from fabledassistant.services.llm import keep_alive_for
         async with httpx.AsyncClient(timeout=120.0) as client:
             await client.post(
                 f"{Config.OLLAMA_URL}/api/chat",
@@ -32,7 +38,7 @@ async def _prime_kv_cache_bg(user_id: int, model: str) -> None:
                     "messages": messages,
                     "stream": False,
                     "options": {"num_predict": 1, "num_ctx": num_ctx},
-                    "keep_alive": "2h",
+                    "keep_alive": keep_alive_for(model),
                 },
             )
         logger.info("Primed KV cache for user %d with model '%s' (num_ctx=%d)", user_id, model, num_ctx)
@@ -58,6 +64,15 @@ async def update_settings_route():
     if not isinstance(data, dict):
         return jsonify({"error": "Expected a JSON object"}), 400
 
+    # Normalize model names to lowercase before validation. Ollama's /api/tags
+    # preserves whatever casing was used at pull time, but /api/chat rejects
+    # mixed-case tags — lowercasing here keeps the two paths consistent and
+    # means the stored setting is always in a form Ollama will actually accept.
+    _MODEL_KEYS = frozenset({"default_model", "background_model"})
+    for _key in _MODEL_KEYS:
+        if _key in data and data[_key]:
+            data[_key] = str(data[_key]).lower()
+
     if "default_model" in data:
         installed = await get_installed_models()
         if data["default_model"]:
@@ -68,7 +83,6 @@ async def update_settings_route():
     # Empty string for model keys means "reset to system default".
     # Delete the DB row so get_setting() falls back to Config defaults
     # rather than returning "" and breaking model resolution everywhere.
-    _MODEL_KEYS = frozenset({"default_model", "background_model"})
     to_save = {}
     for k, v in data.items():
         str_v = str(v)

src/fabledassistant/services/article_context.py

@@ -0,0 +1,270 @@
+"""Prepare article bodies as conversation-ready context.
+
+Used by the briefing ``discuss-article`` flow and the ``/news`` discuss button.
+A raw trafilatura extraction is often too large to drop whole into a chat
+history without eating the context window, so this module runs a map-reduce
+step over oversized articles and returns a compact, structured context that
+still preserves the article's meaning across sections.
+
+Small articles pass through unchanged — map-reduce only fires when the raw
+body exceeds CHAR_BUDGET. The output is cached on ``rss_items.context_prepared``
+by the caller, so repeat discuss-clicks on the same article skip this work
+entirely.
+
+The module also owns ``seed_article_discussion``, the shared routine that
+stages a synthetic ``read_article`` tool exchange plus a conversational seed
+prompt into a conversation. Both the briefing and ``/news`` entry points call
+it so the two flows stay byte-identical — the only thing that differs between
+them is whether the conversation already existed or was freshly created.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import re
+
+from fabledassistant.models import async_session
+from fabledassistant.models.rss_feed import RssItem
+from fabledassistant.services.chat import add_message
+from fabledassistant.services.llm import generate_completion
+
+logger = logging.getLogger(__name__)
+
+# ~12k tokens at 4 chars/token. Comfortably under OLLAMA_NUM_CTX=16384
+# with room left for system prompt, chat history, and the assistant reply.
+CHAR_BUDGET = 48_000
+
+# Chunk size for the map step on oversized articles. Overlap preserves
+# context across paragraph boundaries that happen to land mid-sentence.
+CHUNK_CHARS = 8_000
+CHUNK_OVERLAP = 400
+
+_PARA_SPLIT = re.compile(r"\n\s*\n")
+
+
+def _chunk_by_paragraph(body: str) -> list[str]:
+    """Split ``body`` into chunks of up to CHUNK_CHARS, respecting paragraphs.
+
+    Paragraphs longer than CHUNK_CHARS are split mid-paragraph as a last
+    resort. Adjacent chunks share CHUNK_OVERLAP chars of trailing text so
+    a sentence straddling the boundary stays readable on both sides.
+    """
+    paragraphs = [p.strip() for p in _PARA_SPLIT.split(body) if p.strip()]
+    chunks: list[str] = []
+    current: list[str] = []
+    current_len = 0
+    for para in paragraphs:
+        para_len = len(para)
+        if para_len > CHUNK_CHARS:
+            if current:
+                chunks.append("\n\n".join(current))
+                current, current_len = [], 0
+            for i in range(0, para_len, CHUNK_CHARS - CHUNK_OVERLAP):
+                chunks.append(para[i : i + CHUNK_CHARS])
+            continue
+        if current_len + para_len + 2 > CHUNK_CHARS and current:
+            chunks.append("\n\n".join(current))
+            tail = current[-1][-CHUNK_OVERLAP:] if current else ""
+            current = [tail, para] if tail else [para]
+            current_len = len(tail) + para_len + (2 if tail else 0)
+        else:
+            current.append(para)
+            current_len += para_len + 2
+    if current:
+        chunks.append("\n\n".join(current))
+    return chunks
+
+
+async def _summarize_chunk(title: str, chunk: str, index: int, total: int, model: str) -> str:
+    """Map-step summary of one article chunk.
+
+    Aims for ~300 words of dense, factual prose — not bullet points — so the
+    downstream chat model can quote from it naturally.
+    """
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are summarizing one section of a larger article so a downstream "
+                "conversation model can discuss the full article without having to read "
+                "every word.\n\n"
+                "Requirements:\n"
+                "- 250–350 words of dense factual prose\n"
+                "- Preserve specific claims, numbers, names, and quotes\n"
+                "- Do NOT editorialize or add analysis\n"
+                "- Do NOT use bullet points or headings\n"
+                "- Do NOT say 'this section' or 'this article' — write content, not meta"
+            ),
+        },
+        {
+            "role": "user",
+            "content": (
+                f"Article: {title}\n"
+                f"Section {index + 1} of {total}:\n\n{chunk}"
+            ),
+        },
+    ]
+    try:
+        # Pin num_ctx — same rationale as services/research.py:66. A large
+        # chunk plus system prompt can push well past the default window;
+        # silent truncation here would drop the tail of the chunk without
+        # any error, producing a misleading summary.
+        raw = await generate_completion(
+            messages, model, max_tokens=600, num_ctx=16384
+        )
+        return raw.strip()
+    except Exception:
+        logger.warning(
+            "Article chunk summary failed for section %d/%d of '%s'",
+            index + 1, total, title, exc_info=True,
+        )
+        # Fall back to the raw chunk truncated to ~1500 chars so the overall
+        # pipeline still delivers something rather than dropping the section.
+        return chunk[:1500]
+
+
+async def prepare_article_context(
+    title: str,
+    url: str,
+    body: str,
+    model: str,
+) -> str:
+    """Return a conversation-ready context block for ``body``.
+
+    - Small article (≤ CHAR_BUDGET): returns ``body`` unchanged.
+    - Oversized article: runs a parallel map step over paragraph-aware
+      chunks and concatenates the summaries under section headers.
+
+    The returned string is what should go into the ``read_article`` synthetic
+    tool-result in chat history. Callers are responsible for caching it to
+    ``rss_items.context_prepared``.
+    """
+    body = body or ""
+    if len(body) <= CHAR_BUDGET:
+        return body
+
+    chunks = _chunk_by_paragraph(body)
+    logger.info(
+        "Article '%s' is %d chars, map-reducing into %d chunks",
+        title, len(body), len(chunks),
+    )
+
+    summaries = await asyncio.gather(
+        *[
+            _summarize_chunk(title, chunk, i, len(chunks), model)
+            for i, chunk in enumerate(chunks)
+        ]
+    )
+
+    header = (
+        f"(This article was longer than the chat window could hold verbatim, "
+        f"so the full text was split into {len(chunks)} sections and each was "
+        "summarized below. Each section preserves specific claims, numbers, "
+        "and quotes from the original.)\n\n"
+    )
+    parts = [
+        f"## Section {i + 1}\n\n{summary}"
+        for i, summary in enumerate(summaries)
+    ]
+    return header + "\n\n".join(parts)
+
+
+# Conversational seed prompt for article discussions. Kept here so both the
+# briefing and /news entry points use the exact same wording. See
+# feedback_discuss_prompt_style memory: numbered checklists produce
+# assignment-completion responses; this conversational seed opens a dialogue.
+ARTICLE_DISCUSS_SEED = (
+    "I want to talk about this article. Start with a substantive summary "
+    "of what it's arguing and the key evidence it uses, then tell me what "
+    "stood out to you or seems worth pushing back on. I'll ask follow-ups "
+    "from there."
+)
+
+
+class EmptyArticleError(Exception):
+    """Raised when an article has no extractable body text.
+
+    Callers (the briefing and /news discuss routes) map this to a 422 so the
+    user sees a clear error instead of a hallucinated summary built from an
+    empty synthetic tool result.
+    """
+
+
+async def seed_article_discussion(
+    conv_id: int,
+    item: RssItem,
+    model: str,
+) -> str:
+    """Stage the synthetic read_article tool exchange + conversational seed.
+
+    Used by both the briefing ``discuss_article`` route and the ``/news``
+    ``from-article`` conversation creator. Handles the three-layer cache
+    (``context_prepared`` → ``content_full`` → fresh fetch) and inserts two
+    messages into ``conv_id``:
+
+    1. An assistant message with a synthetic ``read_article`` tool_call whose
+       ``result.content`` carries the prepared article context. The message
+       also carries ``msg_metadata={"rss_item_id": ...}`` so the post-generation
+       hook in ``generation_task.py`` can locate it and persist the first
+       reply as a discussion-summary Note.
+    2. A user message with the shared conversational seed prompt.
+
+    Returns the seed prompt string so callers can pass it to ``run_generation``
+    as ``user_content``.
+    """
+    # Avoid circulars: rss helper imports article_context indirectly nowhere,
+    # but keep this local for symmetry with the route-level imports it
+    # replaces.
+    from fabledassistant.services.rss import get_or_fetch_full_article
+
+    if item.context_prepared:
+        article_content = item.context_prepared
+    else:
+        raw_body = await get_or_fetch_full_article(item) or item.content or ""
+        if not raw_body.strip():
+            # Hard-fail rather than stage an empty synthetic tool result.
+            # An empty `content` field silently tells the model "the article
+            # has nothing in it" and it confabulates from RAG/history. Better
+            # to surface a clean error to the user.
+            logger.warning(
+                "Article discussion aborted: empty body for rss_item %s (%s)",
+                item.id, item.url,
+            )
+            raise EmptyArticleError(
+                "Couldn't extract any readable text from this article."
+            )
+        article_content = await prepare_article_context(
+            item.title or "", item.url, raw_body, model,
+        )
+        if not article_content.strip():
+            raise EmptyArticleError(
+                "Couldn't extract any readable text from this article."
+            )
+        async with async_session() as session:
+            fresh = await session.get(RssItem, item.id)
+            if fresh is not None:
+                fresh.context_prepared = article_content
+                await session.commit()
+
+    synthetic_tool_calls = [{
+        "function": "read_article",
+        "arguments": {"url": item.url},
+        "result": {
+            "success": True,
+            "type": "article_content",
+            "url": item.url,
+            "content": article_content,
+            "truncated": False,
+        },
+    }]
+    await add_message(
+        conv_id,
+        "assistant",
+        "",
+        status="complete",
+        tool_calls=synthetic_tool_calls,
+        msg_metadata={"rss_item_id": item.id, "article_seed": True},
+    )
+    await add_message(conv_id, "user", ARTICLE_DISCUSS_SEED)
+    return ARTICLE_DISCUSS_SEED

src/fabledassistant/services/briefing_conversations.py

@@ -1,12 +1,13 @@
 """Create and manage briefing conversations."""
 
 import logging
-from datetime import date, datetime, timezone
+from datetime import datetime, timezone
 
 from sqlalchemy import select
 
 from fabledassistant.models import async_session
 from fabledassistant.models.conversation import Conversation, Message
+from fabledassistant.services.tz import user_briefing_date
 
 logger = logging.getLogger(__name__)
 
@@ -15,7 +16,10 @@ async def get_or_create_today_conversation(user_id: int, model: str) -> Conversa
     """
     Return today's briefing conversation, creating it if it doesn't exist.
     """
-    today = date.today()
+    # "Today" is the user-local briefing day (flips at 4am local), not
+    # ``date.today()`` — in a UTC container the latter rolls over at
+    # 19:00 NY local and makes the in-progress briefing disappear.
+    today = await user_briefing_date(user_id)
     async with async_session() as session:
         result = await session.execute(
             select(Conversation).where(
@@ -46,8 +50,15 @@ async def post_message(
     role: str,
     content: str,
     metadata: dict | None = None,
+    tool_calls: list | None = None,
 ) -> Message:
-    """Append a message to a briefing conversation."""
+    """Append a message to a briefing conversation.
+
+    ``tool_calls`` is accepted on assistant-role messages so the full
+    agentic briefing sequence (assistant tool-call turns and tool-role
+    results) can be persisted as real conversation rows, keeping the
+    receipts in context on chat follow-ups.
+    """
     async with async_session() as session:
         msg = Message(
             conversation_id=conversation_id,
@@ -55,6 +66,7 @@ async def post_message(
             content=content,
             status="complete",
             msg_metadata=metadata,
+            tool_calls=tool_calls,
         )
         session.add(msg)
         # Bump conversation updated_at

src/fabledassistant/services/briefing_scheduler.py

@@ -191,6 +191,141 @@ async def _auto_pause_stale_projects(user_id: int) -> list[str]:
     return paused
 
 
+async def _persist_agentic_messages(
+    conv_id: int,
+    slot: str,
+    metadata: dict | None,
+) -> None:
+    """Persist the intermediate turns from an agentic briefing run.
+
+    ``metadata["agentic_messages"]`` is the full message list the agent
+    generated — system prompt, user trigger, assistant tool-call turns,
+    tool-role results, and the final assistant prose.
+
+    To stay compatible with the existing chat loader in ``routes/chat.py``,
+    tool results are folded back into the parent assistant message's
+    ``tool_calls[i]["result"]`` field rather than being persisted as
+    separate ``role="tool"`` rows. This matches how regular chat
+    persists agentic turns, so the follow-up chat endpoint can rehydrate
+    the tool sequence using its existing logic.
+
+    Persists everything except the system prompt (implicit in the chat
+    pipeline) and the final assistant prose (the caller posts that
+    separately with the user-facing metadata block). The synthetic user
+    trigger message is persisted so Ollama sees a user→assistant→user
+    sequence rather than an orphaned assistant reply — it's tagged as
+    intermediate so the UI can hide it.
+
+    Legacy (non-agentic) briefings have no ``agentic_messages`` and this
+    function is a no-op.
+    """
+    from fabledassistant.services.briefing_conversations import post_message
+
+    if not metadata:
+        return
+    agentic_messages = metadata.get("agentic_messages") or []
+    if not agentic_messages:
+        return
+
+    # Drop the system prompt (index 0) and the final assistant prose
+    # (last item). The caller posts the final prose as its own message.
+    middle = agentic_messages[1:-1]
+
+    # Walk the middle sequence, pairing each assistant tool-call turn
+    # with the tool-role results that immediately follow it.
+    i = 0
+    n = len(middle)
+    while i < n:
+        m = middle[i]
+        role = m.get("role", "")
+        content = m.get("content", "") or ""
+        tag = {"briefing_slot": slot, "briefing_intermediate": True}
+
+        if role == "assistant" and m.get("tool_calls"):
+            # Collect subsequent tool-role results, matching them
+            # positionally onto this assistant's tool_calls. Normalise
+            # each entry to the flat storage format the chat loader
+            # expects: {"function": <name>, "arguments": <args>,
+            # "result": <result>, "status": "success"|"error"}.
+            raw_tool_calls = list(m["tool_calls"])
+            flat_tool_calls: list[dict] = []
+            result_idx = 0
+            j = i + 1
+
+            import json as _json
+            for raw_tc in raw_tool_calls:
+                fn = raw_tc.get("function") or {}
+                name = fn.get("name") if isinstance(fn, dict) else str(fn)
+                arguments = fn.get("arguments") if isinstance(fn, dict) else {}
+                if isinstance(arguments, str):
+                    try:
+                        arguments = _json.loads(arguments)
+                    except Exception:
+                        arguments = {}
+
+                # Pair up with the next available tool-role message
+                parsed_result: object = {}
+                status = "success"
+                if j < n and middle[j].get("role") == "tool":
+                    tool_content = middle[j].get("content", "") or ""
+                    try:
+                        parsed_result = _json.loads(tool_content)
+                    except Exception:
+                        parsed_result = tool_content
+                    if isinstance(parsed_result, dict) and parsed_result.get("success") is False:
+                        status = "error"
+                    j += 1
+                    result_idx += 1
+
+                flat_tool_calls.append({
+                    "function": name,
+                    "arguments": arguments,
+                    "result": parsed_result,
+                    "status": status,
+                })
+
+            try:
+                await post_message(
+                    conv_id, "assistant", content,
+                    metadata=tag,
+                    tool_calls=flat_tool_calls,
+                )
+            except Exception:
+                logger.warning(
+                    "Failed to persist agentic assistant turn for conv %d slot %s",
+                    conv_id, slot, exc_info=True,
+                )
+            i = j  # skip the tool results we just folded in
+            continue
+
+        if role == "tool":
+            # Unpaired tool result — shouldn't normally happen, but be
+            # defensive and persist it as an assistant-visible note so we
+            # don't lose the receipt entirely.
+            i += 1
+            continue
+
+        if role == "user":
+            # Skip the synthetic user trigger ("Generate my morning briefing…").
+            # Persisting it would recreate the exact "[Midday briefing update]"
+            # problem PR 2 is designed to eliminate: fake user messages
+            # cluttering chat history. The LLM can follow an all-assistant
+            # sequence just fine since the chat endpoint injects the real
+            # system prompt on follow-up.
+            i += 1
+            continue
+
+        # assistant without tool_calls — persist as-is (rare intermediate)
+        try:
+            await post_message(conv_id, role, content, metadata=tag)
+        except Exception:
+            logger.warning(
+                "Failed to persist agentic %s message for conv %d slot %s",
+                role, conv_id, slot, exc_info=True,
+            )
+        i += 1
+
+
 async def _run_slot_for_user(user_id: int, slot: str) -> None:
     """Execute one slot job for one user."""
     from fabledassistant.services.briefing_conversations import (
@@ -248,14 +383,28 @@ async def _run_slot_for_user(user_id: int, slot: str) -> None:
         conv = await get_or_create_today_conversation(user_id, model)
         text, metadata = await run_compilation(user_id, slot, model)
         if text:
-            await post_message(conv.id, "assistant", text, metadata=metadata)
+            # Persist the agentic tool-call sequence as its own messages
+            # so follow-up chat can see the receipts. Each intermediate
+            # message is tagged with briefing_slot so the chat context
+            # loader can decide whether to include them in history.
+            await _persist_agentic_messages(conv.id, slot, metadata)
+            final_meta = {k: v for k, v in metadata.items() if k != "agentic_messages"}
+            final_meta["briefing_slot"] = slot
+            await post_message(conv.id, "assistant", text, metadata=final_meta)
 
     else:
         conv = await get_or_create_today_conversation(user_id, model)
-        text = await run_slot_injection(user_id, slot, model)
+        text, slot_metadata = await run_slot_injection(user_id, slot, model)
         if text:
-            await post_message(conv.id, "user", f"[{slot.title()} briefing update]")
-            await post_message(conv.id, "assistant", text)
+            # No more synthetic "[Midday briefing update]" user-role
+            # messages. Slot updates are plain assistant messages tagged
+            # with briefing_slot so the chat endpoint can filter them
+            # from the LLM's view of history on follow-ups (they remain
+            # visible in the UI).
+            await _persist_agentic_messages(conv.id, slot, slot_metadata)
+            final_meta = {k: v for k, v in slot_metadata.items() if k != "agentic_messages"}
+            final_meta["briefing_slot"] = slot
+            await post_message(conv.id, "assistant", text, metadata=final_meta)
 
     try:
         from fabledassistant.services.push import send_push_notification
@@ -295,10 +444,13 @@ async def _run_profile_closeout(user_id: int, model: str) -> None:
     and append them to the briefing profile note.
     """
     from fabledassistant.services.user_profile import append_observations
-    from fabledassistant.services.briefing_pipeline import _llm_synthesise
+    from fabledassistant.services.llm import generate_completion
+    from fabledassistant.services.tz import user_today
     from fabledassistant.models.conversation import Conversation, Message
 
-    yesterday = (date.today() - timedelta(days=1))
+    # User-local "yesterday" so closeout always targets the day that just
+    # ended in the user's timezone, regardless of container TZ.
+    yesterday = (await user_today(user_id)) - timedelta(days=1)
     async with async_session() as session:
         result = await session.execute(
             select(Conversation).where(
@@ -328,7 +480,17 @@ async def _run_profile_closeout(user_id: int, model: str) -> None:
         "Example: '- User skipped news about finance', '- Prefers weather for home location first'. "
         "If nothing notable, output only: (nothing to note)"
     )
-    observations = await _llm_synthesise(system, transcript, model)
+    try:
+        observations = (await generate_completion(
+            [
+                {"role": "system", "content": system},
+                {"role": "user", "content": transcript},
+            ],
+            model,
+        )).strip()
+    except Exception:
+        logger.warning("Profile closeout synthesis failed for user %d", user_id, exc_info=True)
+        observations = ""
     if observations and "(nothing to note)" not in observations.lower():
         await append_observations(user_id, observations)
 

src/fabledassistant/services/briefing_tools.py

@@ -0,0 +1,9 @@
+"""Briefing tool subset — delegates to the registry's ``briefing=True`` filter.
+
+Tools are opted-in to briefings via ``@tool(briefing=True)`` in their
+respective module, so there is no separate allowlist to maintain here.
+"""
+
+from fabledassistant.services.tools import get_briefing_tools
+
+__all__ = ["get_briefing_tools"]

src/fabledassistant/services/chat.py

@@ -187,6 +187,7 @@ async def add_message(
     context_note_id: int | None = None,
     status: str | None = None,
     tool_calls: list | None = None,
+    msg_metadata: dict | None = None,
 ) -> Message:
     async with async_session() as session:
         kwargs: dict = dict(
@@ -199,6 +200,8 @@ async def add_message(
             kwargs["status"] = status
         if tool_calls is not None:
             kwargs["tool_calls"] = tool_calls
+        if msg_metadata is not None:
+            kwargs["msg_metadata"] = msg_metadata
         msg = Message(**kwargs)
         session.add(msg)
         # Touch conversation updated_at

src/fabledassistant/services/events.py

@@ -7,7 +7,7 @@ import uuid
 from datetime import datetime, timedelta, timezone
 
 from dateutil.rrule import rrulestr
-from sqlalchemy import or_, select
+from sqlalchemy import and_, or_, select
 
 from fabledassistant.models import async_session
 from fabledassistant.models.event import Event
@@ -85,19 +85,30 @@ async def list_events(
     dicts (same shape as Event.to_dict()).
     """
     async with async_session() as session:
+        # Match strategy:
+        #   - Recurring events: fetch all, expand via rrule below.
+        #   - Non-recurring with an end_dt: standard overlap — starts before
+        #     date_to and ends after date_from.
+        #   - Non-recurring with no end_dt: treat as a point event at
+        #     start_dt, include only if start_dt falls within the window.
+        #     (Previously this branch matched any event with a null end_dt,
+        #     returning all past events as "happening today".)
         result = await session.execute(
             select(Event).where(
                 Event.user_id == user_id,
-                # Base window: non-recurring events must overlap range;
-                # recurring events always need to be fetched so they can be expanded.
                 or_(
                     Event.recurrence.isnot(None),
-                    Event.start_dt <= date_to,
-                ),
-                or_(
-                    Event.end_dt.is_(None),
-                    Event.end_dt >= date_from,
-                    Event.recurrence.isnot(None),
+                    and_(
+                        Event.recurrence.is_(None),
+                        Event.start_dt <= date_to,
+                        or_(
+                            Event.end_dt >= date_from,
+                            and_(
+                                Event.end_dt.is_(None),
+                                Event.start_dt >= date_from,
+                            ),
+                        ),
+                    ),
                 ),
             ).order_by(Event.start_dt)
         )

src/fabledassistant/services/generation_task.py

@@ -36,73 +36,157 @@ _TOOL_CALL_MARKER = re.compile(r"^\s*\[TOOL_CALLS\]\s*", re.IGNORECASE)
 
 DB_FLUSH_INTERVAL = 5.0  # seconds between partial DB flushes
 
-# ---------------------------------------------------------------------------
-# Conditional thinking classifier
-# ---------------------------------------------------------------------------
 
-# Patterns that force think=True even on short messages
-_THINK_FORCE = re.compile(
-    r"\b("
-    r"analyz|compar|explain\s+why|help\s+me\s+(think|plan|understand|figure\s+out)|"
-    r"step[- ]by[- ]step|debug|troubleshoot|diagnos|"
-    r"pros\s+and\s+cons|trade[- ]?off|"
-    r"architect|design\s+(a|the|my|this)|"
-    r"write\s+a\s+(detailed|long|comprehensive|full)|"
-    r"brainstorm|outline\s+(a|the|my)|"
-    r"what\s+(are|is)\s+the\s+(best|difference|relationship|impact|implication)|"
-    r"how\s+(do|does|should|would|can)\s+.{0,40}\s+work|"
-    r"why\s+(is|are|does|do|did|would|should)\b"
-    r")",
-    re.IGNORECASE,
+async def _maybe_save_article_discussion_note(
+    user_id: int, conv_id: int, reply_content: str,
+) -> None:
+    """Persist a seeded article-discussion's first reply as a Note.
+
+    Fires after ``run_generation`` completes. Looks for a synthetic
+    read_article seed message on the conversation; if found AND the linked
+    ``rss_items`` row has no ``discussion_note_id`` yet, saves ``reply_content``
+    as a Note, tags it, and writes the backlink. Subsequent discuss clicks on
+    the same article are a no-op (already linked).
+
+    Failures are logged and swallowed — the chat UI should never break because
+    Note persistence hit a snag.
+    """
+    try:
+        if not reply_content or not reply_content.strip():
+            return
+        from sqlalchemy import select as _select
+        from fabledassistant.models.conversation import Message as _Message
+        from fabledassistant.models.rss_feed import RssItem as _RssItem
+        from fabledassistant.services.notes import create_note
+
+        async with async_session() as session:
+            result = await session.execute(
+                _select(_Message)
+                .where(_Message.conversation_id == conv_id)
+                .order_by(_Message.id.asc())
+            )
+            messages = result.scalars().all()
+            seed_meta = None
+            for m in messages:
+                meta = m.msg_metadata or {}
+                if meta.get("article_seed") and meta.get("rss_item_id"):
+                    seed_meta = meta
+                    break
+            if seed_meta is None:
+                return
+            item_id = int(seed_meta["rss_item_id"])
+            item = await session.get(_RssItem, item_id)
+            if item is None or item.discussion_note_id is not None:
+                return
+            article_title = (item.title or "Untitled article").strip()
+            article_url = item.url
+            article_topics = list(item.topics or [])
+
+        note_title = f"Article: {article_title}"[:200]
+        body_parts = [f"**Source:** {article_url}"] if article_url else []
+        body_parts.append(reply_content.strip())
+        note_body = "\n\n".join(body_parts)
+        tags = ["article-summary"] + [t for t in article_topics if t]
+        note = await create_note(
+            user_id=user_id,
+            title=note_title,
+            body=note_body,
+            tags=tags,
+            entity_meta={
+                "source": "article_discussion",
+                "rss_item_id": item_id,
+                "url": article_url,
+                "conversation_id": conv_id,
+            },
+        )
+
+        async with async_session() as session:
+            fresh = await session.get(_RssItem, item_id)
+            if fresh is not None and fresh.discussion_note_id is None:
+                fresh.discussion_note_id = note.id
+                await session.commit()
+        logger.info(
+            "Saved article-discussion summary as note %d for rss_item %d (conv %d)",
+            note.id, item_id, conv_id,
+        )
+    except Exception:
+        logger.warning(
+            "Failed to persist article-discussion note for conv %d",
+            conv_id, exc_info=True,
+        )
+
+# ---------------------------------------------------------------------------
+# Thinking decision
+# ---------------------------------------------------------------------------
+#
+# `_should_think` is the single source of truth for whether a qwen3-class
+# model should engage chain-of-thought for a given request. Frontend callers
+# should NOT hardcode think=True — leave it False and let the classifier
+# decide from message content. An explicit think_requested=True still acts
+# as an override for callers (e.g. a future UI toggle or MCP client) that
+# want to force extended reasoning regardless of content.
+#
+# Why gate it: on qwen3:14b, thinking adds 5–20s of latency before the first
+# visible content token, and most conversational messages do not benefit.
+# Gating by content keeps quick chats fast while preserving reasoning depth
+# for prompts that actually need it.
+#
+# Models that don't support extended reasoning (e.g. llama3, mistral) simply
+# ignore the `think` parameter in the Ollama chat request, so the decision
+# here is harmless on non-thinking models.
+
+
+# Keywords that strongly suggest the user wants reasoning / analysis. Matched
+# case-insensitively as whole-ish phrases.
+_THINK_KEYWORDS: tuple[str, ...] = (
+    "why", "how does", "how do i", "how would", "how should",
+    "explain", "analyze", "analyse", "compare", "contrast",
+    "design", "architect", "architecture", "plan out", "strategize",
+    "debug", "diagnose", "troubleshoot", "root cause",
+    "review", "critique", "evaluate", "trade-off", "tradeoff", "trade off",
+    "pros and cons", "step by step", "walk me through",
+    "prove", "derive", "figure out", "work through",
+    "discuss",  # covers briefing /discuss-article + /discuss-topic entry points
 )
 
-# Patterns that force think=False regardless of message length
-_THINK_SKIP = re.compile(
-    r"^(hi|hey|hello|thanks|thank\s+you|ok|okay|got\s+it|sounds\s+good|"
-    r"great|perfect|sure|yes|no|yep|nope|nice|cool|awesome|"
-    r"what('s| is) \d|what time|how many|remind me|add (a |an )?(task|note|reminder)|"
-    r"create (a |an )?(task|note)|delete|update|mark .{0,30} (done|complete))\b",
-    re.IGNORECASE,
-)
+# Messages shorter than this and without any think-keyword are treated as
+# simple/conversational and skip the thinking phase.
+_SHORT_MESSAGE_CHARS = 80
 
-_WORD_COUNT_THRESHOLD = 60  # messages over this word count always use think=True
-_SHORT_MESSAGE_THRESHOLD = 12  # messages under this always use think=False
+# Messages longer than this are treated as substantive regardless of keywords.
+_LONG_MESSAGE_CHARS = 400
 
 
 def _should_think(user_content: str, think_requested: bool) -> bool:
     """Return whether extended thinking should be used for this request.
 
-    If the caller didn't request thinking, we never enable it. If they did,
-    we check whether the message is complex enough to warrant the overhead.
+    ``think_requested`` acts as an explicit override: if True, thinking is
+    forced on regardless of content. If False (the default), the decision is
+    made by inspecting the message: long or keyword-bearing messages get
+    thinking; short conversational messages skip it.
     """
-    if not think_requested:
-        return False
-
-    text = user_content.strip()
-    word_count = len(text.split())
-
-    if word_count <= _SHORT_MESSAGE_THRESHOLD:
-        return False
-    if _THINK_SKIP.match(text):
-        return False
-    if word_count >= _WORD_COUNT_THRESHOLD:
+    if think_requested:
         return True
-    if "```" in text:
+    text = (user_content or "").strip()
+    if not text:
+        return False
+    if len(text) >= _LONG_MESSAGE_CHARS:
         return True
-    if _THINK_FORCE.search(text):
+    lowered = text.lower()
+    if any(kw in lowered for kw in _THINK_KEYWORDS):
         return True
-
+    if len(text) < _SHORT_MESSAGE_CHARS:
+        return False
+    # Medium-length message with no obvious reasoning cue: default off.
     return False
 
 
 # Human-readable labels for each tool, shown in the status indicator
 _TOOL_LABELS: dict[str, str] = {
-    "create_task": "Creating task",
-    "create_note": "Creating note",
-    "update_note": "Updating note",
-    "delete_note": "Deleting note",
-    "delete_task": "Deleting task",
-    "get_note": "Reading note",
+    "create_note": "Creating note/task",
+    "update_note": "Updating note/task",
+    "delete_note": "Deleting note/task",
+    "read_note": "Reading note",
     "list_notes": "Listing notes",
     "list_tasks": "Searching tasks",
     "search_notes": "Searching notes (semantic)",
@@ -275,25 +359,32 @@ async def run_generation(
         voice_speech_style=voice_speech_style,
     )
 
-    # Pick the smallest context tier that fits the current messages.
+    # Pick the smallest context tier that fits the current messages AND the
+    # tool schemas (which can be 6-10K tokens on their own with ~40 tools).
     # Using the minimum needed tier reduces KV cache size and speeds up prefill.
-    num_ctx = pick_num_ctx(messages)
+    num_ctx = pick_num_ctx(messages, tools=tools)
     logger.debug("Adaptive num_ctx=%d for conv %d", num_ctx, conv_id)
 
     # Emit context event
     buf.append_event("context", {"context": context_meta})
 
-    # Apply thinking classifier — downgrade think=True for simple/conversational messages
-    think = _should_think(user_content, think)
+    # `_should_think` is authoritative — frontend callers pass think=False by
+    # default and let this classifier decide based on message content. An
+    # explicit think=True still forces on as an override.
+    think_requested = think
+    think = _should_think(user_content, think_requested)
 
     t_start = time.monotonic()
     timing: dict = {
+        "think_requested": think_requested,
         "think": think,
         "num_ctx": num_ctx,
         "tools": [],
         "rounds": 0,
         "prompt_tokens": None,
         "output_tokens": None,
+        "first_token_ms": None,
+        "thinking_ms": None,
         "ttft_ms": None,
         "generation_ms": None,
         "total_ms": None,
@@ -325,11 +416,21 @@ async def run_generation(
                     break
 
                 if chunk.type == "thinking":
+                    if timing["first_token_ms"] is None:
+                        timing["first_token_ms"] = int((time.monotonic() - t_start) * 1000)
                     buf.append_event("thinking_chunk", {"chunk": chunk.content})
 
                 elif chunk.type == "content":
                     if timing["ttft_ms"] is None:
-                        timing["ttft_ms"] = int((time.monotonic() - t_start) * 1000)
+                        now_ms = int((time.monotonic() - t_start) * 1000)
+                        timing["ttft_ms"] = now_ms
+                        if timing["first_token_ms"] is None:
+                            # No thinking phase occurred — first token IS content.
+                            timing["first_token_ms"] = now_ms
+                        else:
+                            # Thinking phase duration = gap between first thinking
+                            # token and first content token.
+                            timing["thinking_ms"] = now_ms - timing["first_token_ms"]
                     buf.content_so_far += chunk.content
                     clean = _TOOL_CALL_MARKER.sub("", chunk.content)
                     if clean:
@@ -376,8 +477,9 @@ async def run_generation(
                                 buf.content_so_far += done_text
                             except Exception as e:
                                 logger.exception("Research pipeline failed for topic: %s", topic)
-                                result = {"success": False, "error": str(e)}
-                                err_text = f"\nResearch failed: {e}"
+                                err_msg = str(e) or f"{type(e).__name__}: unexpected error"
+                                result = {"success": False, "error": err_msg}
+                                err_text = f"\nResearch failed: {err_msg}"
                                 buf.append_event("chunk", {"chunk": err_text})
                                 buf.content_so_far += err_text
                             research_completed = True
@@ -452,9 +554,13 @@ async def run_generation(
 
         timing["total_ms"] = int((time.monotonic() - t_start) * 1000)
         logger.info(
-            "Generation timing for conv %d: total=%dms ttft=%s tools=%s generation=%s",
-            conv_id, timing["total_ms"], timing["ttft_ms"],
-            [(t["name"], t["ms"]) for t in timing["tools"]], timing["generation_ms"],
+            "Generation timing for conv %d: total=%dms think=%s(req=%s) first_token=%s "
+            "thinking=%s ttft=%s generation=%s tools=%s",
+            conv_id, timing["total_ms"],
+            timing["think"], timing["think_requested"],
+            timing["first_token_ms"], timing["thinking_ms"], timing["ttft_ms"],
+            timing["generation_ms"],
+            [(t["name"], t["ms"]) for t in timing["tools"]],
         )
         try:
             await log_generation(user_id, conv_id, model, timing)
@@ -499,10 +605,35 @@ async def run_generation(
         msg_count = len(non_system)
         should_gen_title = not conv_title or (msg_count > 0 and msg_count % 10 == 0)
 
+        # Persist article-discussion seed conversations as a Note on their
+        # first assistant reply. This makes "Discuss" summaries part of RAG
+        # so the knowledge base stops being amnesiac about articles the user
+        # has already engaged with. The hook detects a seeded conversation by
+        # finding a synthetic read_article assistant message whose
+        # msg_metadata carries ``article_seed: True`` and whose rss_items row
+        # has no discussion_note_id yet. Fire-and-forget so the done event
+        # lands immediately.
+        asyncio.create_task(_maybe_save_article_discussion_note(
+            user_id, conv_id, buf.content_so_far,
+        ))
+
         if should_gen_title:
-            title_messages = messages + [
-                {"role": "assistant", "content": buf.content_so_far}
+            # Feed the title model the *raw* conversation turns only — never
+            # the post-build_context ``messages`` list. ``build_context``
+            # prepends RAG snippets, RSS excerpts, URL content, and briefing
+            # article dumps INTO the user message string itself, so filtering
+            # by role="user" downstream still surfaces that noise as the
+            # "user's message". That pollution caused wildly-wrong titles
+            # (bug #109) — the small background model was staring at article
+            # excerpts instead of what the user actually typed. Pass the
+            # original history + the raw user_content + the assistant reply.
+            title_messages: list[dict] = [
+                {"role": m["role"], "content": m.get("content") or ""}
+                for m in history
+                if m.get("role") in ("user", "assistant")
             ]
+            title_messages.append({"role": "user", "content": user_content})
+            title_messages.append({"role": "assistant", "content": buf.content_so_far})
 
             async def _bg_title() -> None:
                 try:

src/fabledassistant/services/llm.py

@@ -26,12 +26,32 @@ logger = logging.getLogger(__name__)
 _CTX_TIERS = (8192, 16384, 32768)
 
 
-def pick_num_ctx(messages: list[dict]) -> int:
-    """Return the smallest context tier that fits *messages* with 25% headroom.
+def keep_alive_for(model: str) -> str:
+    """Return the Ollama keep_alive duration for *model*.
+
+    Background models get a shorter window because they're called
+    sporadically; the main interactive model gets a longer one so it
+    stays warm between user messages.
+    """
+    if model == Config.OLLAMA_BACKGROUND_MODEL:
+        return Config.OLLAMA_KEEP_ALIVE_BACKGROUND
+    return Config.OLLAMA_KEEP_ALIVE_MAIN
+
+
+def pick_num_ctx(messages: list[dict], tools: list[dict] | None = None) -> int:
+    """Return the smallest context tier that fits *messages* + *tools* with 25% headroom.
+
+    The ``tools`` JSON schemas are a large, often-overlooked chunk of the prompt.
+    With ~40 tools in the registry the schemas alone can be 6-10K tokens — enough
+    that omitting them from the estimate causes silent prompt truncation.
 
     Stays at or below Config.OLLAMA_NUM_CTX (the configured ceiling).
     """
     total_chars = sum(len(m.get("content") or "") for m in messages)
+    if tools:
+        # Serialize the same way Ollama will see them. json.dumps gives us a
+        # faithful char count for the schema payload without any guesswork.
+        total_chars += len(json.dumps(tools))
     estimated_tokens = int(total_chars / 3.5)
     needed = int(estimated_tokens * 1.25) + 256  # 25% headroom + output buffer
     cap = Config.OLLAMA_NUM_CTX
@@ -58,7 +78,14 @@ RAG_AUTO_SNIPPET = 4000
 
 
 async def get_installed_models() -> set[str]:
-    """Return set of installed Ollama model names (with and without :latest)."""
+    """Return set of installed Ollama model names (with and without :latest).
+
+    Tags are normalized to lowercase. Ollama stores whatever casing was used at
+    pull time (``ollama pull gemma3:12B`` keeps the ``B``), but inference calls
+    against the capitalized tag can 400 — the two code paths are inconsistent.
+    Lowercasing on read avoids exposing that mixed-case name in the UI and
+    keeps the validation check below from accepting a form Ollama will reject.
+    """
     try:
         async with httpx.AsyncClient(timeout=10.0) as client:
             resp = await client.get(f"{Config.OLLAMA_URL}/api/tags")
@@ -66,7 +93,7 @@ async def get_installed_models() -> set[str]:
             data = resp.json()
             names: set[str] = set()
             for m in data.get("models", []):
-                name = m["name"]
+                name = m["name"].lower()
                 names.add(name)
                 if name.endswith(":latest"):
                     names.add(name.removesuffix(":latest"))
@@ -78,6 +105,9 @@ async def get_installed_models() -> set[str]:
 
 async def ensure_model(model: str) -> None:
     """Check if model exists in Ollama, pull if missing."""
+    # Match the lowercase normalization in get_installed_models so a legacy
+    # mixed-case setting doesn't force a spurious re-pull at startup.
+    model = model.lower()
     try:
         installed = await get_installed_models()
         if model in installed or f"{model}:latest" in installed:
@@ -129,6 +159,24 @@ async def wait_for_model_loaded(model: str, timeout: float = 90.0) -> bool:
         await asyncio.sleep(min(2.0, remaining))
 
 
+async def _raise_ollama_error(resp: httpx.Response, model: str) -> None:
+    """On a non-2xx Ollama response, log its body before raising.
+
+    ``resp.raise_for_status()`` alone throws away Ollama's error body, which
+    carries the actual reason (e.g. ``"model does not support tools"``,
+    ``"context length exceeded"``). Reading the body first means failures
+    surface a usable message instead of a bare HTTPStatusError.
+    """
+    if resp.status_code < 400:
+        return
+    body = (await resp.aread()).decode("utf-8", errors="replace").strip()
+    logger.error(
+        "Ollama /api/chat %d for model=%s: %s",
+        resp.status_code, model, body[:500],
+    )
+    resp.raise_for_status()
+
+
 async def stream_chat(
     messages: list[dict],
     model: str,
@@ -145,7 +193,7 @@ async def stream_chat(
     merged_options = {"num_ctx": num_ctx or Config.OLLAMA_NUM_CTX}
     if options:
         merged_options.update(options)
-    payload: dict = {"model": model, "messages": messages, "stream": True, "options": merged_options, "think": think, "keep_alive": "2h"}
+    payload: dict = {"model": model, "messages": messages, "stream": True, "options": merged_options, "think": think, "keep_alive": keep_alive_for(model)}
     # read=None: no per-chunk timeout — Ollama may pause for any duration while
     # processing a large input context before the first token arrives.
     async with httpx.AsyncClient(timeout=httpx.Timeout(connect=30.0, read=None, write=None, pool=30.0)) as client:
@@ -154,7 +202,7 @@ async def stream_chat(
             f"{Config.OLLAMA_URL}/api/chat",
             json=payload,
         ) as resp:
-            resp.raise_for_status()
+            await _raise_ollama_error(resp, model)
             async for line in resp.aiter_lines():
                 if not line.strip():
                     continue
@@ -204,7 +252,7 @@ async def stream_chat_with_tools(
         "stream": True,
         "options": options,
         "think": think,
-        "keep_alive": "2h",
+        "keep_alive": keep_alive_for(model),
     }
     if tools:
         payload["tools"] = tools
@@ -215,7 +263,7 @@ async def stream_chat_with_tools(
             f"{Config.OLLAMA_URL}/api/chat",
             json=payload,
         ) as resp:
-            resp.raise_for_status()
+            await _raise_ollama_error(resp, model)
             accumulated_tool_calls: list[dict] = []
             async for line in resp.aiter_lines():
                 if not line.strip():
@@ -269,9 +317,17 @@ async def generate_completion(
     num_ctx overrides the model's context window for this call only.
     """
     last_exc: Exception | None = None
-    options: dict = {"num_predict": max_tokens}
-    if num_ctx is not None:
-        options["num_ctx"] = num_ctx
+    # Default num_ctx to Config.OLLAMA_NUM_CTX (matching stream_chat /
+    # stream_chat_with_tools). Without this, Ollama silently uses the model's
+    # default window (~4k on qwen3) and truncates anything longer. That is
+    # how the research pipeline's outline step kept falling back to a single
+    # monolith note: its 12-source prompt is ~6k tokens and was being chopped
+    # before the model ever saw it. Non-streaming callers must not inherit
+    # that footgun — if you truly want the model default, pass num_ctx=0.
+    options: dict = {
+        "num_predict": max_tokens,
+        "num_ctx": num_ctx if num_ctx is not None else Config.OLLAMA_NUM_CTX,
+    }
     for attempt in range(3):
         if attempt > 0:
             delay = 3.0 * attempt
@@ -289,7 +345,7 @@ async def generate_completion(
                         "stream": False,
                         "think": False,
                         "options": options,
-                        "keep_alive": "2h",
+                        "keep_alive": keep_alive_for(model),
                     },
                 )
                 resp.raise_for_status()
@@ -510,6 +566,8 @@ async def build_context(
     tool_lines = [
         "You have access to tool functions. You MUST use them when the user asks you to create, add, find, schedule, or search for anything.",
         "CRITICAL: Call the tool functions directly. NEVER write out function calls as text or code. NEVER describe what you would do — just do it.",
+        "GROUNDING: When the user asks about their own data — tasks, notes, events, projects, news, anything stored in this system — call the relevant tool to see what actually exists before answering. Never assert facts about the user's data from memory, prior context, or assumption. If you are unsure whether something exists, check with a tool.",
+        "HONESTY WHEN EMPTY: If a tool returns empty results (no matching tasks, no events in the date range, no search hits, no notes found), tell the user plainly that nothing matched. Do not fabricate example items, do not invent plausible-sounding meetings or deadlines to fill the response, and do not hedge with generic suggestions dressed up as real data. A direct 'you don't have anything on your calendar today' is always better than an invented event.",
         "Available actions: create_task, create_note, update_note, delete_note, delete_task, get_note, list_notes, list_tasks, search_notes.",
     ]
     if has_caldav:
@@ -666,18 +724,27 @@ async def build_context(
     orphan_only = rag_project_id is None
     effective_project_id = rag_project_id if (rag_project_id is not None and rag_project_id != -1) else None
 
-    try:
-        from fabledassistant.services.embeddings import semantic_search_notes
-        for score, note in await semantic_search_notes(
-            user_id, user_message, exclude_ids=search_exclude or None, limit=8,
-            project_id=effective_project_id,
-            orphan_only=orphan_only,
-        ):
-            found_scored.append((score, note))
-    except Exception:
-        logger.warning("Semantic note search failed, falling back to keyword search", exc_info=True)
+    # Skip RAG auto-injection on the first turn of a seeded article discussion.
+    # The article body is already the sole context the user wants — pulling in
+    # unrelated orphan notes tricks the model into summarizing those instead.
+    # Follow-up turns keep RAG on because by then the user's own messages drive
+    # the query rather than the generic seed prompt.
+    from fabledassistant.services.article_context import ARTICLE_DISCUSS_SEED
+    _skip_rag_for_article_seed = user_message.strip() == ARTICLE_DISCUSS_SEED
 
-    if not found_scored:
+    if not _skip_rag_for_article_seed:
+        try:
+            from fabledassistant.services.embeddings import semantic_search_notes
+            for score, note in await semantic_search_notes(
+                user_id, user_message, exclude_ids=search_exclude or None, limit=8,
+                project_id=effective_project_id,
+                orphan_only=orphan_only,
+            ):
+                found_scored.append((score, note))
+        except Exception:
+            logger.warning("Semantic note search failed, falling back to keyword search", exc_info=True)
+
+    if not found_scored and not _skip_rag_for_article_seed:
         keywords = _extract_keywords(user_message)
         if keywords:
             try:

src/fabledassistant/services/notes.py

@@ -136,6 +136,7 @@ async def list_notes(
     milestone_ids: list[int] | None = None,
     parent_id: int | None = None,
     no_project: bool = False,
+    exclude_paused_projects: bool = False,
     sort: str = "updated_at",
     order: str = "desc",
     limit: int = 50,
@@ -211,6 +212,17 @@ async def list_notes(
             query = query.where(Note.project_id.is_(None))
             count_query = count_query.where(Note.project_id.is_(None))
 
+        if exclude_paused_projects:
+            from fabledassistant.models.project import Project
+            paused_ids = (
+                select(Project.id)
+                .where(Project.user_id == user_id, Project.status == "paused")
+                .scalar_subquery()
+            )
+            paused_filter = or_(Note.project_id.is_(None), Note.project_id.not_in(paused_ids))
+            query = query.where(paused_filter)
+            count_query = count_query.where(paused_filter)
+
         sort_col = getattr(Note, sort, Note.updated_at)
         if order == "asc":
             query = query.order_by(sort_col.asc())

src/fabledassistant/services/projects.py

@@ -141,13 +141,30 @@ async def generate_project_summary(user_id: int, project_id: int) -> None:
         logger.debug("Failed to generate summary for project %d", project_id, exc_info=True)
 
 
+# Cutoff: summaries older than this were generated by qwen2.5:3b, which
+# produced broken output (token repetition, hallucinated topics, misspellings).
+# Anything stamped before the switch to gemma3:4b on 2026-04-12 gets
+# regenerated at startup so the whole project list ends up on the better model.
+_BACKGROUND_MODEL_CUTOVER = datetime(2026, 4, 12, tzinfo=timezone.utc)
+
+
 async def backfill_project_summaries() -> None:
-    """Generate summaries for all projects missing auto_summary. Fire-and-forget."""
+    """Generate summaries for projects missing or predating the gemma3:4b cutover.
+
+    Fire-and-forget: each summary runs as its own background task.
+    """
     import asyncio
+    from sqlalchemy import or_
     try:
         async with async_session() as session:
             rows = (await session.execute(
-                select(Project.id, Project.user_id).where(Project.auto_summary.is_(None))
+                select(Project.id, Project.user_id).where(
+                    or_(
+                        Project.auto_summary.is_(None),
+                        Project.summary_updated_at.is_(None),
+                        Project.summary_updated_at < _BACKGROUND_MODEL_CUTOVER,
+                    )
+                )
             )).all()
         for row in rows:
             asyncio.create_task(generate_project_summary(row.user_id, row.id))

src/fabledassistant/services/research.py

@@ -9,7 +9,7 @@ import httpx
 
 from fabledassistant.config import Config
 from fabledassistant.services.llm import fetch_url_content, generate_completion, stream_chat
-from fabledassistant.services.notes import create_note
+from fabledassistant.services.notes import create_note, update_note
 from fabledassistant.models.note import Note
 
 logger = logging.getLogger(__name__)
@@ -35,8 +35,8 @@ def _build_sources_block(sources: list[dict]) -> str:
 async def _generate_outline(topic: str, sources: list[dict], model: str) -> list[dict]:
     """Generate a topic outline from fetched research sources.
 
-    Returns a list of {"title": str, "focus": str} dicts (3–8 entries).
-    Returns [] on failure — callers must fall back to single-note synthesis.
+    Returns a list of {"title": str, "focus": str} dicts (2–8 entries).
+    Retries once on failure before returning [] (callers fall back to single-note).
     """
     import json as _json
 
@@ -61,23 +61,37 @@ async def _generate_outline(topic: str, sources: list[dict], model: str) -> list
             "content": f"Topic: {topic}\n\nSources:\n{sources_block}",
         },
     ]
-    try:
-        raw = await generate_completion(messages, model, max_tokens=400)
-        raw = raw.strip()
-        raw = re.sub(r"^```(?:json)?\s*", "", raw)
-        raw = re.sub(r"\s*```$", "", raw)
-        idx = raw.find("[")
-        if idx >= 0:
-            parsed, _ = _json.JSONDecoder().raw_decode(raw[idx:])
-            if isinstance(parsed, list):
-                sections = [
-                    s for s in parsed
-                    if isinstance(s, dict) and s.get("title") and s.get("focus")
-                ]
-                if len(sections) >= 3:
-                    return sections[:8]
-    except Exception:
-        logger.warning("Outline generation failed for topic '%s'", topic, exc_info=True)
+    for attempt in range(2):
+        try:
+            # Pin num_ctx explicitly. The prompt carries up to 12 sources at
+            # 2000 chars each (~6k tokens of source material alone) plus the
+            # system prompt — well over Ollama's default model window on
+            # qwen3. Without this, Ollama silently truncates the prompt, the
+            # model can't see most of the sources, JSON parsing fails twice,
+            # and the pipeline falls back to a single monolith note
+            # (`research.py:251`). Do not remove even if `generate_completion`
+            # appears to default this — see the comment there.
+            raw = await generate_completion(
+                messages, model, max_tokens=400, num_ctx=16384
+            )
+            raw = raw.strip()
+            raw = re.sub(r"^```(?:json)?\s*", "", raw)
+            raw = re.sub(r"\s*```$", "", raw)
+            idx = raw.find("[")
+            if idx >= 0:
+                parsed, _ = _json.JSONDecoder().raw_decode(raw[idx:])
+                if isinstance(parsed, list):
+                    sections = [
+                        s for s in parsed
+                        if isinstance(s, dict) and s.get("title") and s.get("focus")
+                    ]
+                    if len(sections) >= 2:
+                        return sections[:8]
+        except Exception:
+            logger.warning(
+                "Outline generation attempt %d failed for topic '%s'",
+                attempt + 1, topic, exc_info=True,
+            )
     return []
 
 
@@ -121,6 +135,55 @@ async def _synthesize_section(
     return section_title, raw.strip()
 
 
+async def _generate_executive_summary(
+    topic: str,
+    section_bodies: list[tuple[str, str]],
+    model: str,
+) -> str:
+    """Generate a 2-3 paragraph executive summary from completed section notes.
+
+    Args:
+        section_bodies: list of (title, body) pairs from the section notes.
+
+    Returns summary markdown (no heading — caller adds structure).
+    """
+    sections_block = "\n\n".join(
+        f"### {title}\n{body[:1500]}" for title, body in section_bodies
+    )
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are a research summarizer. Given several completed research sections on a topic, "
+                "write a concise executive summary.\n\n"
+                "Requirements:\n"
+                "- 2–3 paragraphs of substantive prose (150–300 words total)\n"
+                "- Cover the key findings and insights across all sections\n"
+                "- Highlight the most important or surprising takeaways\n"
+                "- Write so someone can decide which sections to read in detail\n"
+                "- Do NOT include headings, bullet points, or source citations\n"
+                "- Do NOT start with 'This research' or 'This document' — jump straight into the content"
+            ),
+        },
+        {
+            "role": "user",
+            "content": f"Topic: {topic}\n\nSections:\n{sections_block}",
+        },
+    ]
+    try:
+        # Pin num_ctx explicitly — see `_generate_outline` comment for the
+        # rationale. This prompt carries N sections × 1500 chars of section
+        # prose, which can easily exceed the default model window. Don't
+        # trust the `generate_completion` default to stick.
+        raw = await generate_completion(
+            messages, model, max_tokens=600, num_ctx=16384
+        )
+        return raw.strip()
+    except Exception:
+        logger.warning("Executive summary generation failed for '%s'", topic, exc_info=True)
+        return ""
+
+
 async def run_research_pipeline(
     topic: str,
     user_id: int,
@@ -220,28 +283,17 @@ async def run_research_pipeline(
         return_exceptions=True,
     )
 
-    # Step 5: Create section notes sequentially
-    _status(f"Saving {len(outline)} notes...")
-    section_note_pairs: list[tuple[dict, Note]] = []
+    # Collect successful results for index + summary generation
+    section_results: list[tuple[dict, str, str]] = []  # (outline_entry, title, body)
     for section, result in zip(outline, raw_results):
         if isinstance(result, Exception):
             logger.warning("Section synthesis failed for '%s': %s", section["title"], result)
             continue
         sec_title, sec_body = result
-        try:
-            note = await create_note(
-                user_id=user_id,
-                title=sec_title,
-                body=sec_body,
-                tags=["research"],
-                project_id=project_id,
-            )
-            section_note_pairs.append((section, note))
-        except Exception:
-            logger.warning("Failed to save section note '%s'", sec_title, exc_info=True)
+        section_results.append((section, sec_title, sec_body))
 
     # All sections failed — fall back to single note
-    if not section_note_pairs:
+    if not section_results:
         logger.warning("All section syntheses failed, falling back to single note for '%s'", topic)
         _status("Synthesizing report (fallback)...")
         title, body = await _synthesize_note(topic, synthesis_sources, model, buf=None)
@@ -250,20 +302,24 @@ async def run_research_pipeline(
         )
         return note
 
-    # Step 6: Create index note
+    # Step 5: Generate executive summary from section content
+    _status("Writing summary...")
+    executive_summary = await _generate_executive_summary(
+        topic, [(t, b) for _, t, b in section_results], model,
+    )
+
+    # Step 6: Create index note first (so section notes can reference it via parent_id)
     from datetime import date as _date
     index_lines = [
         f"Research overview for **{topic}** — {_date.today().isoformat()}",
         "",
-        f"Generated from {len(synthesis_sources)} web sources across {len(section_note_pairs)} sections.",
-        "",
-        "## Sections",
+        f"Generated from {len(synthesis_sources)} web sources across {len(section_results)} sections.",
         "",
     ]
-    for section, note in section_note_pairs:
-        index_lines.append(f"- **{note.title}** — {section['focus']}")
-    index_lines += ["", "*Search for any section title to read it.*"]
-
+    if executive_summary:
+        index_lines += ["## Summary", "", executive_summary, ""]
+    index_lines += ["## Sections", ""]
+    # Placeholder — will be updated with real links after section notes are created
     index_note = await create_note(
         user_id=user_id,
         title=f"Research: {topic}",
@@ -271,6 +327,34 @@ async def run_research_pipeline(
         tags=["research", "research-index"],
         project_id=project_id,
     )
+
+    # Step 7: Create section notes with parent_id pointing to index
+    _status(f"Saving {len(section_results)} notes...")
+    section_note_pairs: list[tuple[dict, Note]] = []
+    for section, sec_title, sec_body in section_results:
+        try:
+            note = await create_note(
+                user_id=user_id,
+                title=sec_title,
+                body=sec_body,
+                tags=["research"],
+                project_id=project_id,
+                parent_id=index_note.id,
+            )
+            section_note_pairs.append((section, note))
+        except Exception:
+            logger.warning("Failed to save section note '%s'", sec_title, exc_info=True)
+
+    # Step 8: Update index note body with real links to section notes
+    for section, note in section_note_pairs:
+        index_lines.append(f"- [{note.title}](/notes/{note.id}) — {section['focus']}")
+
+    await update_note(
+        user_id=user_id,
+        note_id=index_note.id,
+        body="\n".join(index_lines),
+    )
+
     logger.info(
         "Research: created %d section notes + index id=%d for topic '%s'",
         len(section_note_pairs), index_note.id, topic,

src/fabledassistant/services/rss.py

@@ -34,6 +34,34 @@ def _html_to_text(html: str) -> str:
         return html
 
 
+async def get_or_fetch_full_article(item: RssItem) -> str | None:
+    """Return the full article body, fetching+caching on miss.
+
+    Checks ``item.content_full`` first — populated either by the enrichment
+    pass at feed-ingest time or by a previous discuss-click. On miss, fetches
+    via ``_fetch_full_article`` and writes through. Returns ``None`` only if
+    the fetch itself fails; ``item.content_full == ""`` is still a cache hit.
+
+    Callers must pass an RssItem attached to an open session if they want
+    the write-through to persist — otherwise the fetched text is returned
+    but the cache stays empty and the next click will re-fetch.
+    """
+    if item.content_full is not None:
+        return item.content_full
+    if not item.url:
+        return None
+    text = await _fetch_full_article(item.url)
+    if text is None:
+        return None
+    async with async_session() as session:
+        fresh = await session.get(RssItem, item.id)
+        if fresh is not None:
+            fresh.content_full = text
+            fresh.content_fetched_at = datetime.now(timezone.utc)
+            await session.commit()
+    return text
+
+
 async def _fetch_full_article(url: str) -> str | None:
     """Fetch a URL and extract its main article text via trafilatura.
 
@@ -209,6 +237,11 @@ async def fetch_and_cache_feed(feed_id: int, url: str) -> int:
                         item = await session.get(RssItem, item_id)
                         if item:
                             item.content = full_text
+                            # Populate the discuss-click cache too so the
+                            # first click skips straight to the map-reduce
+                            # step without re-fetching.
+                            item.content_full = full_text
+                            item.content_fetched_at = datetime.now(timezone.utc)
                             await session.commit()
                             await upsert_rss_item_embedding(
                                 item_id, feed_user_id, item.title or "", item.content

src/fabledassistant/services/tools/__init__.py

@@ -0,0 +1,33 @@
+"""Tool registry package.
+
+Importing this package loads all tool modules (triggering ``@tool``
+decorator registration) and re-exports the public API that the rest
+of the app depends on.
+"""
+
+# Import every tool module so their @tool decorators run at import time.
+# Order does not matter — registration is additive.
+from fabledassistant.services.tools import (  # noqa: F401
+    calendar,
+    entities,
+    notes,
+    profile,
+    projects,
+    rag,
+    rss,
+    tasks,
+    utility,
+    weather,
+    web,
+)
+from fabledassistant.services.tools._registry import (
+    execute_tool,
+    get_briefing_tools,
+    get_tools_for_user,
+)
+
+__all__ = [
+    "execute_tool",
+    "get_briefing_tools",
+    "get_tools_for_user",
+]

src/fabledassistant/services/tools/_helpers.py

@@ -0,0 +1,124 @@
+"""Shared utilities used across tool modules."""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import re
+from datetime import date, datetime
+from difflib import SequenceMatcher
+
+logger = logging.getLogger(__name__)
+
+_PUNCT_RE = re.compile(r"[^\w\s]")
+
+
+def schedule_embedding(note_id: int, user_id: int, title: str, body: str) -> None:
+    """Fire-and-forget: update the embedding for a note after it's created/modified."""
+    from fabledassistant.services.embeddings import upsert_note_embedding
+
+    text = f"{title}\n{body}".strip() if body else (title or "")
+    if text:
+        asyncio.create_task(upsert_note_embedding(note_id, user_id, text))
+
+
+async def resolve_project(user_id: int, project_name: str):
+    """Exact-then-fuzzy project lookup. Returns the Project or None.
+
+    Resolution order:
+    1. Exact title match (case-insensitive via DB)
+    2. project_name is a substring of an existing title
+    3. Existing title is a substring of project_name
+    4. SequenceMatcher ratio >= 0.55
+    """
+    from fabledassistant.services.projects import get_project_by_title, list_projects
+
+    proj = await get_project_by_title(user_id, project_name)
+    if proj is not None:
+        return proj
+    needle = project_name.lower().strip()
+    all_p = await list_projects(user_id)
+    for p in all_p:
+        haystack = p.title.lower().strip()
+        if needle in haystack or haystack in needle:
+            return p
+    best, best_r = None, 0.0
+    for p in all_p:
+        r = SequenceMatcher(None, needle, p.title.lower().strip()).ratio()
+        if r >= 0.55 and r > best_r:
+            best, best_r = p, r
+    return best
+
+
+def parse_due_date(value: str | None) -> date | None:
+    """Parse a due date string, returning None on failure."""
+    if not value:
+        return None
+    try:
+        return datetime.strptime(value, "%Y-%m-%d").date()
+    except (ValueError, TypeError):
+        logger.warning("Invalid due_date format: %s", value)
+        return None
+
+
+def fuzzy_title_match(title: str, candidates, threshold: float = 0.82):
+    """Return (best_match, ratio) if any candidate's title is similar enough.
+
+    Uses SequenceMatcher ratio. Threshold 0.82 catches near-duplicates like
+    "Game Premise" / "Game Premise Notes" while leaving clearly different
+    titles alone. Returns (None, 0.0) when no candidate meets the threshold.
+    """
+    needle = title.lower().strip()
+    best, best_r = None, 0.0
+    for c in candidates:
+        r = SequenceMatcher(None, needle, c.title.lower().strip()).ratio()
+        if r >= threshold and r > best_r:
+            best, best_r = c, r
+    return best, best_r
+
+
+async def check_duplicate(
+    user_id: int,
+    title: str,
+    body: str,
+    is_task: bool,
+    confirmed: bool,
+) -> dict | None:
+    """Check for exact, fuzzy, and semantic duplicates. Returns error dict or None."""
+    from fabledassistant.services.notes import list_notes
+
+    item_label = "task" if is_task else "note"
+
+    existing, _ = await list_notes(user_id=user_id, q=title, is_task=is_task, limit=1)
+    exact = next((n for n in existing if n.title.lower() == title.lower()), None)
+    if exact is not None:
+        return {
+            "success": False,
+            "error": f"A {item_label} titled '{title}' already exists (id: {exact.id}). Use update_note to modify it instead of creating a duplicate.",
+        }
+
+    clean_q = _PUNCT_RE.sub(" ", title).strip()
+    candidates, _ = await list_notes(user_id=user_id, q=clean_q, is_task=is_task, limit=20)
+    near, ratio = fuzzy_title_match(title, candidates)
+    if near is not None:
+        return {
+            "success": False,
+            "requires_confirmation": True,
+            "similar_note": {"id": near.id, "title": near.title},
+            "error": f"A {item_label} with a very similar title '{near.title}' already exists (similarity: {ratio:.0%}). Ask the user to confirm before creating a separate entry.",
+        }
+
+    if not confirmed and len(body.strip()) >= 80:
+        from fabledassistant.services.embeddings import semantic_search_notes as _ssn
+        sem_query = f"{title}\n{body}".strip()
+        sem_hits = await _ssn(user_id, sem_query, limit=3, threshold=0.90, is_task=is_task)
+        if sem_hits:
+            best_score, best_note = sem_hits[0]
+            return {
+                "success": False,
+                "requires_confirmation": True,
+                "similar_note": {"id": best_note.id, "title": best_note.title},
+                "error": f"A {item_label} with very similar content exists: '{best_note.title}' (semantic similarity: {best_score:.0%}). Ask the user to confirm before creating a separate entry.",
+            }
+
+    return None

src/fabledassistant/services/tools/_registry.py

@@ -0,0 +1,133 @@
+"""Decorator-based tool registry.
+
+Each tool is registered via ``@tool(...)`` which captures the OpenAI-style
+function schema and handler in one place.  ``get_tools_for_user`` and
+``execute_tool`` are the public API consumed by the rest of the app.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from typing import Any, Callable, Coroutine
+
+from fabledassistant.config import Config
+from fabledassistant.services.caldav import is_caldav_configured
+
+logger = logging.getLogger(__name__)
+
+type ToolHandler = Callable[..., Coroutine[Any, Any, dict]]
+
+
+@dataclass(frozen=True, slots=True)
+class ToolDef:
+    name: str
+    description: str
+    parameters: dict
+    handler: ToolHandler
+    read_only: bool = False
+    briefing: bool = False
+    requires: str | None = None
+    required_params: list[str] = field(default_factory=list)
+
+    def schema(self) -> dict:
+        """Return the OpenAI function-calling schema dict."""
+        return {
+            "type": "function",
+            "function": {
+                "name": self.name,
+                "description": self.description,
+                "parameters": {
+                    "type": "object",
+                    "properties": self.parameters,
+                    **({"required": self.required_params} if self.required_params else {}),
+                },
+            },
+        }
+
+
+_REGISTRY: dict[str, ToolDef] = {}
+
+
+def tool(
+    *,
+    name: str,
+    description: str,
+    parameters: dict | None = None,
+    required: list[str] | None = None,
+    read_only: bool = False,
+    briefing: bool = False,
+    requires: str | None = None,
+) -> Callable[[ToolHandler], ToolHandler]:
+    """Register an async tool handler with its schema metadata."""
+
+    def decorator(fn: ToolHandler) -> ToolHandler:
+        if name in _REGISTRY:
+            raise ValueError(f"Duplicate tool registration: {name}")
+        _REGISTRY[name] = ToolDef(
+            name=name,
+            description=description,
+            parameters=parameters or {},
+            handler=fn,
+            read_only=read_only,
+            briefing=briefing,
+            requires=requires,
+            required_params=required or [],
+        )
+        return fn
+
+    return decorator
+
+
+async def _check_requires(user_id: int, requires: str) -> bool:
+    if requires == "caldav":
+        return await is_caldav_configured(user_id)
+    if requires == "searxng":
+        return Config.searxng_enabled()
+    return True
+
+
+async def get_tools_for_user(user_id: int) -> list[dict]:
+    """Build the tool schema list for a user based on configured integrations."""
+    tools: list[dict] = []
+    for td in _REGISTRY.values():
+        if td.requires and not await _check_requires(user_id, td.requires):
+            continue
+        tools.append(td.schema())
+    logger.debug("User %d: %d tools available", user_id, len(tools))
+    return tools
+
+
+async def get_briefing_tools(user_id: int) -> list[dict]:
+    """Return only the tool schemas marked ``briefing=True``."""
+    all_tools = await get_tools_for_user(user_id)
+    names = {td.name for td in _REGISTRY.values() if td.briefing}
+    filtered = [t for t in all_tools if t["function"]["name"] in names]
+    logger.debug(
+        "Briefing tools for user %d: %d of %d selected",
+        user_id, len(filtered), len(all_tools),
+    )
+    return filtered
+
+
+async def execute_tool(
+    user_id: int,
+    tool_name: str,
+    arguments: dict,
+    conv_id: int | None = None,
+    workspace_project_id: int | None = None,
+) -> dict:
+    """Execute a tool call and return the result."""
+    td = _REGISTRY.get(tool_name)
+    if td is None:
+        return {"success": False, "error": f"Unknown tool: {tool_name}"}
+    try:
+        return await td.handler(
+            user_id=user_id,
+            arguments=arguments,
+            conv_id=conv_id,
+            workspace_project_id=workspace_project_id,
+        )
+    except Exception as e:
+        logger.exception("Tool execution failed: %s", tool_name)
+        return {"success": False, "error": str(e)}

src/fabledassistant/services/tools/calendar.py

@@ -0,0 +1,250 @@
+"""Calendar and event tools."""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+
+from fabledassistant.services.events import (
+    create_event as events_create_event,
+    delete_event as events_delete_event,
+    find_events_by_query,
+    list_events as events_list_events,
+    search_events as events_search_events,
+    update_event as events_update_event,
+)
+from fabledassistant.services.tools._helpers import resolve_project
+from fabledassistant.services.tools._registry import tool
+from fabledassistant.services.tz import get_user_tz
+
+
+async def _parse_datetime_in_user_tz(
+    user_id: int, value: str
+) -> tuple[datetime, bool]:
+    """Parse a date/datetime string from the model into a UTC-aware datetime.
+
+    Naive inputs are interpreted in the **user's local timezone** and then
+    converted to UTC for storage. Never default to UTC for naive inputs —
+    that's how all-day events landed on the wrong day for non-UTC users.
+
+    Returns ``(utc_datetime, was_date_only)``.
+    """
+    was_date_only = "T" not in value and " " not in value
+    if was_date_only:
+        value = f"{value}T00:00:00"
+    dt = datetime.fromisoformat(value.replace("Z", "+00:00"))
+    if dt.tzinfo is None:
+        user_tz = await get_user_tz(user_id)
+        dt = dt.replace(tzinfo=user_tz)
+    return dt.astimezone(timezone.utc), was_date_only
+
+
+@tool(
+    name="create_event",
+    description="Create a calendar event for the user. Use this when the user asks to schedule, add, or create a meeting, appointment, or event. Pass dates and datetimes in the user's local time — a bare date like '2026-09-30' is interpreted as that day in the user's configured timezone.",
+    parameters={
+        "title": {"type": "string", "description": "A descriptive event title"},
+        "start": {"type": "string", "description": "Start date (YYYY-MM-DD) or datetime (YYYY-MM-DDTHH:MM) in the user's local time. Include a timezone offset only if the user explicitly mentions one."},
+        "end": {"type": "string", "description": "Optional end date or datetime in the user's local time"},
+        "duration": {"type": "integer", "description": "Optional duration in minutes (default 60, ignored if end is set or all_day is true)"},
+        "description": {"type": "string", "description": "Optional event description"},
+        "location": {"type": "string", "description": "Optional event location"},
+        "color": {"type": "string", "description": "Optional hex color for the event (e.g. '#6366f1')"},
+        "all_day": {"type": "boolean", "description": "True for all-day events (birthdays, holidays, deadlines)"},
+        "recurrence": {"type": "string", "description": "Optional iCalendar RRULE (e.g. 'FREQ=YEARLY' for annual, 'FREQ=WEEKLY' for weekly, 'FREQ=MONTHLY' for monthly)"},
+        "reminder_minutes": {"type": "integer", "description": "Optional reminder N minutes before the event (e.g. 30 for 30 minutes before)"},
+        "attendees": {"type": "array", "items": {"type": "string"}, "description": "Optional list of attendee email addresses"},
+        "calendar_name": {"type": "string", "description": "Optional calendar name to create the event in. Falls back to default calendar."},
+        "project": {"type": "string", "description": "Optional project name to associate this event with"},
+    },
+    required=["title", "start"],
+)
+async def create_event_tool(*, user_id, arguments, **_ctx):
+    start_str = arguments["start"]
+    end_str = arguments.get("end")
+    all_day = arguments.get("all_day", False)
+    try:
+        # Naive dates/datetimes are interpreted in the user's local
+        # timezone, not UTC. Storing UTC for naive inputs caused all-day
+        # events to land on the previous day for negative-offset users.
+        start_dt, start_was_date_only = await _parse_datetime_in_user_tz(
+            user_id, start_str
+        )
+    except (ValueError, TypeError):
+        return {"success": False, "error": f"Invalid start datetime: {start_str!r}"}
+    if start_was_date_only:
+        all_day = True
+    end_dt = None
+    if end_str:
+        try:
+            end_dt, _ = await _parse_datetime_in_user_tz(user_id, end_str)
+        except (ValueError, TypeError):
+            return {"success": False, "error": f"Invalid end datetime: {end_str!r}"}
+    project_id = None
+    project_name = arguments.get("project")
+    if project_name:
+        proj = await resolve_project(user_id, project_name)
+        if proj:
+            project_id = proj.id
+    event = await events_create_event(
+        user_id=user_id,
+        title=arguments.get("title", "Untitled Event"),
+        start_dt=start_dt,
+        end_dt=end_dt,
+        all_day=all_day,
+        description=arguments.get("description") or "",
+        location=arguments.get("location") or "",
+        color=arguments.get("color") or "",
+        recurrence=arguments.get("recurrence"),
+        project_id=project_id,
+        duration=arguments.get("duration"),
+        reminder_minutes=arguments.get("reminder_minutes"),
+        attendees=arguments.get("attendees"),
+        calendar_name=arguments.get("calendar_name"),
+    )
+    return {"success": True, "type": "event", "data": event.to_dict()}
+
+
+@tool(
+    name="list_events",
+    description="List calendar events in a date range. Use this when the user asks what events or meetings they have. Pass plain local dates (YYYY-MM-DD) — the server interprets them in the user's timezone and expands to a full local day.",
+    parameters={
+        "date_from": {"type": "string", "description": "Start of range as a local date (YYYY-MM-DD) or local datetime. Interpreted in the user's timezone."},
+        "date_to": {"type": "string", "description": "End of range as a local date (YYYY-MM-DD) or local datetime. A bare date is expanded to 23:59:59 local."},
+    },
+    required=["date_from", "date_to"],
+    read_only=True,
+    briefing=True,
+)
+async def list_events_tool(*, user_id, arguments, **_ctx):
+    # Bare local dates are expanded to a full local day in the user's TZ:
+    # date_from → 00:00 local, date_to → 23:59:59 local, both converted to
+    # UTC before the DB query. Previously the tool description told the
+    # model to pass UTC ranges, which missed events for non-UTC users.
+    try:
+        date_from, _ = await _parse_datetime_in_user_tz(
+            user_id, arguments["date_from"]
+        )
+        date_to_str = arguments["date_to"]
+        if "T" not in date_to_str and " " not in date_to_str:
+            date_to_str = f"{date_to_str}T23:59:59"
+        date_to, _ = await _parse_datetime_in_user_tz(user_id, date_to_str)
+    except (ValueError, TypeError, KeyError) as exc:
+        return {"success": False, "error": f"Invalid date range: {exc}"}
+    events = await events_list_events(user_id=user_id, date_from=date_from, date_to=date_to)
+    return {
+        "success": True,
+        "type": "events",
+        "data": {"count": len(events), "events": events},
+    }
+
+
+@tool(
+    name="search_events",
+    description="Search calendar events by keyword. Use this when the user asks to find a specific event or meeting.",
+    parameters={
+        "query": {"type": "string", "description": "Search keyword to match against event titles, locations, and descriptions"},
+        "include_past": {"type": "boolean", "description": "Set to true to include past events in results (default: future events only)"},
+    },
+    required=["query"],
+    read_only=True,
+    briefing=True,
+)
+async def search_events_tool(*, user_id, arguments, **_ctx):
+    events = await events_search_events(
+        user_id=user_id,
+        query=arguments.get("query", ""),
+        include_past=arguments.get("include_past", False),
+    )
+    return {
+        "success": True,
+        "type": "events",
+        "data": {
+            "query": arguments.get("query", ""),
+            "count": len(events),
+            "events": [e.to_dict() for e in events],
+        },
+    }
+
+
+@tool(
+    name="update_event",
+    description="Update an existing calendar event. Use this when the user asks to change, move, reschedule, or modify an event.",
+    parameters={
+        "query": {"type": "string", "description": "Search term to find the event to update (matches against title)"},
+        "title": {"type": "string", "description": "New title for the event"},
+        "start": {"type": "string", "description": "New start datetime in ISO 8601 format"},
+        "end": {"type": "string", "description": "New end datetime in ISO 8601 format"},
+        "all_day": {"type": "boolean", "description": "Whether the event is all-day"},
+        "description": {"type": "string", "description": "New event description"},
+        "location": {"type": "string", "description": "New event location"},
+        "color": {"type": "string", "description": "New hex color for the event (e.g. '#6366f1')"},
+        "recurrence": {"type": "string", "description": "New iCalendar RRULE"},
+        "reminder_minutes": {"type": "integer", "description": "Reminder N minutes before the event. Pass 0 to remove an existing reminder."},
+    },
+    required=["query"],
+)
+async def update_event_tool(*, user_id, arguments, **_ctx):
+    query = arguments.get("query", "")
+    matches = await find_events_by_query(user_id=user_id, query=query)
+    if not matches:
+        return {"success": False, "error": f"No event found matching '{query}'."}
+    event_to_update = matches[0]
+    fields: dict = {}
+    for str_field in ("title", "description", "location", "color", "recurrence"):
+        if arguments.get(str_field) is not None:
+            fields[str_field] = arguments[str_field]
+    if arguments.get("all_day") is not None:
+        fields["all_day"] = arguments["all_day"]
+    if "reminder_minutes" in arguments:
+        rm = arguments["reminder_minutes"]
+        fields["reminder_minutes"] = None if rm == 0 else rm
+    for dt_field, key in (("start_dt", "start"), ("end_dt", "end")):
+        val = arguments.get(key)
+        if val:
+            try:
+                # Naive datetimes are user-local, not UTC — see
+                # ``_parse_datetime_in_user_tz`` docstring.
+                dt, _ = await _parse_datetime_in_user_tz(user_id, val)
+                fields[dt_field] = dt
+            except (ValueError, TypeError):
+                return {"success": False, "error": f"Invalid datetime for {key}: {val!r}"}
+    updated = await events_update_event(user_id=user_id, event_id=event_to_update.id, **fields)
+    if updated is None:
+        return {"success": False, "error": "Event not found or update failed."}
+    return {"success": True, "type": "event_updated", "data": updated.to_dict()}
+
+
+@tool(
+    name="delete_event",
+    description="Delete a calendar event. Use this when the user asks to cancel, remove, or delete an event.",
+    parameters={
+        "query": {"type": "string", "description": "Search term to find the event to delete (matches against title)"},
+    },
+    required=["query"],
+)
+async def delete_event_tool(*, user_id, arguments, **_ctx):
+    query = arguments.get("query", "")
+    matches = await find_events_by_query(user_id=user_id, query=query)
+    if not matches:
+        return {"success": False, "error": f"No event found matching '{query}'."}
+    event_to_delete = matches[0]
+    await events_delete_event(user_id=user_id, event_id=event_to_delete.id)
+    return {"success": True, "type": "event_deleted", "data": {"id": event_to_delete.id, "title": event_to_delete.title}}
+
+
+@tool(
+    name="list_calendars",
+    description="List all available calendars. Use this when the user asks which calendars they have.",
+    parameters={},
+    read_only=True,
+    requires="caldav",
+)
+async def list_calendars_tool(*, user_id, arguments, **_ctx):
+    from fabledassistant.services.caldav import list_calendars
+
+    calendars = await list_calendars(user_id=user_id)
+    return {
+        "success": True,
+        "type": "calendars",
+        "data": {"count": len(calendars), "calendars": calendars},
+    }

src/fabledassistant/services/tools/entities.py

@@ -0,0 +1,231 @@
+"""Entity tools: people, places, and lists."""
+
+from __future__ import annotations
+
+from fabledassistant.services.notes import create_note, list_notes, update_note
+from fabledassistant.services.tools._helpers import schedule_embedding
+from fabledassistant.services.tools._registry import tool
+
+
+def _build_person_body(meta: dict, extra_notes: str | None = None) -> str:
+    """Build markdown body from person metadata."""
+    lines = []
+    for key, label in (("relationship", "Relationship"), ("phone", "Phone"), ("email", "Email"), ("birthday", "Birthday"), ("address", "Address")):
+        if meta.get(key):
+            lines.append(f"**{label}:** {meta[key]}")
+    if extra_notes:
+        lines.append(f"\n{extra_notes}")
+    return "\n".join(lines)
+
+
+def _build_place_body(meta: dict, extra_notes: str | None = None) -> str:
+    """Build markdown body from place metadata."""
+    lines = []
+    for key, label in (("address", "Address"), ("phone", "Phone"), ("hours", "Hours"), ("url", "Website")):
+        if meta.get(key):
+            lines.append(f"**{label}:** {meta[key]}")
+    if extra_notes:
+        lines.append(f"\n{extra_notes}")
+    return "\n".join(lines)
+
+
+_PERSON_FIELDS = ("relationship", "phone", "email", "birthday", "address")
+_PLACE_FIELDS = ("address", "phone", "hours", "url")
+
+
+@tool(
+    name="save_person",
+    description=(
+        "Save or update a person in the user's knowledge base. Creates a new entry if the "
+        "person doesn't exist, or updates an existing one. Use when the user introduces "
+        "someone by name or adds/corrects details about a known person."
+    ),
+    parameters={
+        "name": {"type": "string", "description": "Full name of the person (used to find existing entry or as new entry title)"},
+        "relationship": {"type": "string", "description": "Relationship to the user (e.g. daughter, dentist, colleague)"},
+        "phone": {"type": "string", "description": "Phone number"},
+        "email": {"type": "string", "description": "Email address"},
+        "birthday": {"type": "string", "description": "Birthday in YYYY-MM-DD format"},
+        "address": {"type": "string", "description": "Home or mailing address"},
+        "notes": {"type": "string", "description": "Any additional free-form notes about this person"},
+    },
+    required=["name"],
+)
+async def save_person_tool(*, user_id, arguments, **_ctx):
+    name = str(arguments.get("name", "")).strip()
+    if not name:
+        return {"success": False, "error": "name is required"}
+
+    existing, _ = await list_notes(user_id=user_id, q=name, is_task=False, limit=10)
+    target = next((n for n in existing if n.note_type == "person"), None)
+
+    if target is not None:
+        meta = dict(target.entity_meta or {})
+        for field in _PERSON_FIELDS:
+            val = arguments.get(field)
+            if val is not None:
+                meta[field] = str(val)
+        extra_notes = arguments.get("notes")
+        body = _build_person_body(meta, extra_notes)
+        updated = await update_note(user_id=user_id, note_id=target.id, body=body, entity_meta=meta)
+        if updated is None:
+            return {"success": False, "error": "Failed to update person."}
+        schedule_embedding(target.id, user_id, target.title, body)
+        return {"success": True, "type": "person_updated", "data": {"id": target.id, "name": target.title}}
+
+    meta = {}
+    for field in _PERSON_FIELDS:
+        val = arguments.get(field)
+        if val:
+            meta[field] = str(val)
+    body = _build_person_body(meta, arguments.get("notes", ""))
+    note = await create_note(
+        user_id=user_id, title=name, body=body,
+        tags=["person"], note_type="person", entity_meta=meta,
+    )
+    schedule_embedding(note.id, user_id, name, note.body)
+    return {"success": True, "type": "person", "data": {"id": note.id, "name": name}}
+
+
+@tool(
+    name="save_place",
+    description=(
+        "Save or update a named location in the user's knowledge base. Creates a new entry "
+        "if the place doesn't exist, or updates an existing one. Use when the user wants to "
+        "remember or correct details about a place (business, venue, address)."
+    ),
+    parameters={
+        "name": {"type": "string", "description": "Name of the place (e.g. 'Dr. Smith Dental', 'Costco')"},
+        "address": {"type": "string", "description": "Street address"},
+        "phone": {"type": "string", "description": "Phone number"},
+        "hours": {"type": "string", "description": "Opening hours (e.g. 'Mon-Fri 9am-5pm')"},
+        "url": {"type": "string", "description": "Website URL"},
+        "notes": {"type": "string", "description": "Any additional free-form notes about this place"},
+    },
+    required=["name"],
+)
+async def save_place_tool(*, user_id, arguments, **_ctx):
+    name = str(arguments.get("name", "")).strip()
+    if not name:
+        return {"success": False, "error": "name is required"}
+
+    existing, _ = await list_notes(user_id=user_id, q=name, is_task=False, limit=10)
+    target = next((n for n in existing if n.note_type == "place"), None)
+
+    if target is not None:
+        meta = dict(target.entity_meta or {})
+        for field in _PLACE_FIELDS:
+            val = arguments.get(field)
+            if val is not None:
+                meta[field] = str(val)
+        extra_notes = arguments.get("notes")
+        body = _build_place_body(meta, extra_notes)
+        updated = await update_note(user_id=user_id, note_id=target.id, body=body, entity_meta=meta)
+        if updated is None:
+            return {"success": False, "error": "Failed to update place."}
+        schedule_embedding(target.id, user_id, target.title, body)
+        return {"success": True, "type": "place_updated", "data": {"id": target.id, "name": target.title}}
+
+    meta = {}
+    for field in _PLACE_FIELDS:
+        val = arguments.get(field)
+        if val:
+            meta[field] = str(val)
+    body = _build_place_body(meta, arguments.get("notes", ""))
+    note = await create_note(
+        user_id=user_id, title=name, body=body,
+        tags=["place"], note_type="place", entity_meta=meta,
+    )
+    schedule_embedding(note.id, user_id, name, note.body)
+    return {"success": True, "type": "place", "data": {"id": note.id, "name": name}}
+
+
+@tool(
+    name="create_list",
+    description="Create a named list (shopping list, to-do list, packing list, etc.). Items are stored as a markdown task list. Use add_to_list to add items to an existing list.",
+    parameters={
+        "name": {"type": "string", "description": "List name (e.g. 'Grocery List', 'Hardware Store')"},
+        "items": {"type": "array", "items": {"type": "string"}, "description": "Initial list items (unchecked)"},
+        "store": {"type": "string", "description": "Optional associated store or place name"},
+        "tags": {"type": "array", "items": {"type": "string"}, "description": "Tags for the list"},
+    },
+    required=["name"],
+)
+async def create_list_tool(*, user_id, arguments, **_ctx):
+    name = str(arguments.get("name", "")).strip()
+    if not name:
+        return {"success": False, "error": "name is required"}
+    items = arguments.get("items") or []
+    if not isinstance(items, list):
+        items = []
+    body = "\n".join(f"- [ ] {item}" for item in items if str(item).strip())
+    meta = {}
+    store = arguments.get("store")
+    if store:
+        meta["store"] = str(store)
+    tags = arguments.get("tags") or []
+    if not isinstance(tags, list):
+        tags = []
+    note = await create_note(
+        user_id=user_id, title=name, body=body,
+        tags=tags, note_type="list", entity_meta=meta,
+    )
+    schedule_embedding(note.id, user_id, name, body)
+    return {"success": True, "type": "list", "data": {"id": note.id, "name": name, "item_count": len(items)}}
+
+
+@tool(
+    name="add_to_list",
+    description="Add one or more items to an existing list. Items are appended as unchecked entries.",
+    parameters={
+        "list_name": {"type": "string", "description": "Name of the list to add items to"},
+        "items": {"type": "array", "items": {"type": "string"}, "description": "Items to add"},
+    },
+    required=["list_name", "items"],
+)
+async def add_to_list_tool(*, user_id, arguments, **_ctx):
+    list_name = str(arguments.get("list_name", "")).strip()
+    items = arguments.get("items") or []
+    if not list_name:
+        return {"success": False, "error": "list_name is required"}
+    if not isinstance(items, list) or not items:
+        return {"success": False, "error": "items must be a non-empty array"}
+    existing, _ = await list_notes(user_id=user_id, q=list_name, is_task=False, limit=10)
+    target = next((n for n in existing if n.note_type == "list" and n.title.lower() == list_name.lower()), None)
+    if target is None:
+        target = next((n for n in existing if n.note_type == "list"), None)
+    if target is None:
+        return {"success": False, "error": f"No list named '{list_name}' found. Use create_list to create it first."}
+    new_lines = "\n".join(f"- [ ] {item}" for item in items if str(item).strip())
+    existing_body = (target.body or "").rstrip()
+    new_body = f"{existing_body}\n{new_lines}".lstrip()
+    await update_note(user_id=user_id, note_id=target.id, body=new_body)
+    schedule_embedding(target.id, user_id, target.title, new_body)
+    return {"success": True, "type": "list_updated", "data": {"id": target.id, "name": target.title, "added": len(items)}}
+
+
+@tool(
+    name="clear_checked_items",
+    description="Remove all checked/completed items from a list, keeping only unchecked items.",
+    parameters={
+        "list_name": {"type": "string", "description": "Name of the list to clear"},
+    },
+    required=["list_name"],
+)
+async def clear_checked_items_tool(*, user_id, arguments, **_ctx):
+    import re as _re
+
+    list_name = str(arguments.get("list_name", "")).strip()
+    if not list_name:
+        return {"success": False, "error": "list_name is required"}
+    existing, _ = await list_notes(user_id=user_id, q=list_name, is_task=False, limit=10)
+    target = next((n for n in existing if n.note_type == "list" and n.title.lower() == list_name.lower()), None)
+    if target is None:
+        target = next((n for n in existing if n.note_type == "list"), None)
+    if target is None:
+        return {"success": False, "error": f"No list named '{list_name}' found."}
+    body = target.body or ""
+    cleaned = _re.sub(r"^- \[[xX]\] .+\n?", "", body, flags=_re.MULTILINE).strip()
+    await update_note(user_id=user_id, note_id=target.id, body=cleaned)
+    schedule_embedding(target.id, user_id, target.title, cleaned)
+    return {"success": True, "type": "list_cleared", "data": {"id": target.id, "name": target.title}}

src/fabledassistant/services/tools/notes.py

@@ -0,0 +1,420 @@
+"""Note tools: create, update, get, list, delete, search."""
+
+from __future__ import annotations
+
+import logging
+
+from fabledassistant.services.notes import create_note, delete_note, get_note_by_title, list_notes, update_note
+from fabledassistant.services.tag_suggestions import suggest_tags
+from fabledassistant.services.tools._helpers import (
+    check_duplicate,
+    parse_due_date,
+    resolve_project,
+    schedule_embedding,
+)
+from fabledassistant.services.tools._registry import tool
+
+logger = logging.getLogger(__name__)
+
+
+@tool(
+    name="create_note",
+    description=(
+        "Create a new note or task. "
+        "For a knowledge note, omit the status field. "
+        "For an actionable task (todo, reminder, action item), set status to 'todo'. "
+        "Use this whenever the user asks to write down, save, record, or add a task/todo."
+    ),
+    parameters={
+        "title": {"type": "string", "description": "The title"},
+        "body": {"type": "string", "description": "Content in markdown"},
+        "tags": {"type": "array", "items": {"type": "string"}, "description": 'Tags (without # prefix, hyphens for multi-word: ["science-fiction", "story/idea"]). Do NOT embed #tags in the body.'},
+        "project": {"type": "string", "description": "Optional project name. Only set this if the user explicitly named a project. Do NOT infer a project from the content or context."},
+        "status": {"type": "string", "enum": ["todo", "in_progress", "done", "cancelled"], "description": "Set to 'todo' to create a task. Omit entirely for a knowledge note."},
+        "due_date": {"type": "string", "description": "Due date in YYYY-MM-DD format (tasks only)"},
+        "priority": {"type": "string", "enum": ["none", "low", "medium", "high"], "description": "Priority level (tasks only, default: none)"},
+        "parent_task": {"type": "string", "description": "Title of a parent task to make this a sub-task of"},
+        "milestone": {"type": "string", "description": "Milestone title within the project to assign to"},
+        "recurrence_rule": {"type": "object", "description": 'Recurrence rule (tasks only). Interval form: {"type":"interval","every":3,"unit":"month"} (units: day/week/month/year). Calendar form: {"type":"calendar","unit":"month","day_of_month":1}. Annual: add "month":6.'},
+        "confirmed": {"type": "boolean", "description": "Set to true only when the user has explicitly confirmed creation after being warned about a similar existing item."},
+    },
+    required=["title"],
+)
+async def create_note_tool(*, user_id, arguments, **_ctx):
+    title = arguments.get("title", "Untitled")
+    body = arguments.get("body", "")
+    tags = arguments.get("tags", [])
+    if not isinstance(title, str):
+        return {"success": False, "error": "title must be a string. Call create_note once per item."}
+    if not isinstance(body, str):
+        body = ""
+    if not isinstance(tags, list):
+        tags = []
+
+    is_task = "status" in arguments and arguments["status"] is not None
+    status = arguments.get("status", "todo") if is_task else None
+
+    project_name = arguments.get("project")
+    milestone_name = arguments.get("milestone")
+    parent_task_name = arguments.get("parent_task")
+    project_id = None
+    milestone_id = None
+
+    if project_name:
+        proj = await resolve_project(user_id, project_name)
+        if proj is None:
+            return {"success": False, "error": f"Project '{project_name}' not found. Use list_projects to find the correct project name and retry with the exact name."}
+        project_id = proj.id
+        if milestone_name:
+            from fabledassistant.services.milestones import get_milestone_by_title as _gmbt
+            ms = await _gmbt(user_id, proj.id, milestone_name)
+            if ms is None:
+                return {"success": False, "error": f"Milestone '{milestone_name}' not found in project '{proj.title}'. Use list_milestones to see available milestones."}
+            milestone_id = ms.id
+
+    dup = await check_duplicate(user_id, title, body, is_task=is_task, confirmed=bool(arguments.get("confirmed")))
+    if dup is not None:
+        return dup
+
+    note = await create_note(
+        user_id=user_id,
+        title=title,
+        body=body,
+        tags=tags,
+        status=status,
+        priority=arguments.get("priority", "none") if is_task else None,
+        due_date=parse_due_date(arguments.get("due_date")) if is_task else None,
+        recurrence_rule=arguments.get("recurrence_rule") if is_task else None,
+        project_id=project_id,
+        milestone_id=milestone_id,
+    )
+
+    if parent_task_name:
+        parent_notes, _ = await list_notes(user_id=user_id, q=parent_task_name, is_task=True, limit=1)
+        if parent_notes:
+            note = await update_note(user_id, note.id, parent_id=parent_notes[0].id)
+
+    suggested = await suggest_tags(user_id, title, body)
+    schedule_embedding(note.id, user_id, title, body)
+
+    if is_task:
+        return {
+            "success": True,
+            "type": "task",
+            "data": {
+                "id": note.id,
+                "title": note.title,
+                "status": note.status,
+                "priority": note.priority,
+                "due_date": str(note.due_date) if note.due_date else None,
+                "project_id": note.project_id,
+                "milestone_id": note.milestone_id,
+                "parent_id": note.parent_id,
+            },
+            "suggested_tags": suggested,
+        }
+    return {
+        "success": True,
+        "type": "note",
+        "data": {"id": note.id, "title": note.title, "project_id": note.project_id},
+        "suggested_tags": suggested,
+    }
+
+
+@tool(
+    name="update_note",
+    description="Update an existing note or task — content, title, status, priority, or due date. Use for edits, marking tasks done, changing priority. Never use create_note for existing notes.",
+    parameters={
+        "query": {"type": "string", "description": "Title or keyword to find the note or task to update"},
+        "body": {"type": "string", "description": "New note content in markdown (omit if only updating task fields)"},
+        "title": {"type": "string", "description": "Optional new title"},
+        "mode": {"type": "string", "enum": ["replace", "append"], "description": "How to apply the new body: 'replace' overwrites existing content (default), 'append' adds after existing content"},
+        "status": {"type": "string", "enum": ["todo", "in_progress", "done", "cancelled"], "description": "New task status. Use to mark a task done, start it, cancel it, etc."},
+        "priority": {"type": "string", "enum": ["none", "low", "medium", "high"], "description": "New task priority"},
+        "due_date": {"type": "string", "description": "New due date in YYYY-MM-DD format"},
+        "tags": {"type": "array", "items": {"type": "string"}, "description": "Tags to apply (see tag_mode for how they're applied)"},
+        "tag_mode": {"type": "string", "enum": ["add", "remove", "replace"], "description": "'add' appends tags without duplicates (default), 'remove' removes listed tags, 'replace' sets tags to exactly this list (destructive — avoid unless explicitly requested)"},
+        "project": {"type": "string", "description": "Optional project name to assign this note/task to (set to empty string to remove project)"},
+        "milestone": {"type": "string", "description": "Optional milestone title within the project to assign this task to (set to empty string to remove milestone)"},
+        "recurrence_rule": {"type": "object", "description": 'Set or update recurrence. Interval form: {"type":"interval","every":3,"unit":"month"}. Calendar form: {"type":"calendar","unit":"month","day_of_month":1}. Pass null to remove.'},
+    },
+    required=["query"],
+)
+async def update_note_tool(*, user_id, arguments, **_ctx):
+    query = arguments.get("query", "")
+    new_body = arguments.get("body", "")
+    new_title = arguments.get("title")
+    mode = arguments.get("mode", "replace")
+
+    note = await get_note_by_title(user_id, query)
+    if note is None:
+        notes, _ = await list_notes(user_id=user_id, q=query, limit=5)
+        note_only = [n for n in notes if n.status is None]
+        candidates = note_only or notes
+        if not candidates:
+            return {"success": False, "error": f"No note found matching '{query}'."}
+        note = candidates[0]
+
+    update_fields: dict = {}
+    if new_title:
+        update_fields["title"] = new_title
+    if new_body:
+        if mode == "append" and note.body:
+            update_fields["body"] = note.body + "\n\n" + new_body
+        else:
+            update_fields["body"] = new_body
+    if "status" in arguments:
+        update_fields["status"] = arguments["status"]
+    if "priority" in arguments:
+        update_fields["priority"] = arguments["priority"]
+    if "due_date" in arguments:
+        update_fields["due_date"] = parse_due_date(arguments["due_date"])
+    if "tags" in arguments:
+        new_tags = arguments["tags"]
+        if isinstance(new_tags, list):
+            tag_mode = arguments.get("tag_mode", "add")
+            if tag_mode == "add":
+                existing = list(note.tags or [])
+                for t in new_tags:
+                    if t not in existing:
+                        existing.append(t)
+                update_fields["tags"] = existing
+            elif tag_mode == "remove":
+                existing = list(note.tags or [])
+                update_fields["tags"] = [t for t in existing if t not in new_tags]
+            else:
+                update_fields["tags"] = new_tags
+
+    if "project" in arguments:
+        project_name = arguments["project"]
+        if project_name:
+            proj = await resolve_project(user_id, project_name)
+            if proj is None:
+                return {"success": False, "error": f"Project '{project_name}' not found. Use list_projects to see available projects."}
+            update_fields["project_id"] = proj.id
+        else:
+            update_fields["project_id"] = None
+            update_fields["milestone_id"] = None
+
+    if "milestone" in arguments:
+        milestone_name = arguments["milestone"]
+        if milestone_name:
+            ref_project_id = update_fields.get("project_id") or note.project_id
+            if ref_project_id is None:
+                return {"success": False, "error": "Cannot assign a milestone without a project. Set project first."}
+            from fabledassistant.services.milestones import get_milestone_by_title as _gmbt_upd
+            ms = await _gmbt_upd(user_id, ref_project_id, milestone_name)
+            if ms is None:
+                return {"success": False, "error": f"Milestone '{milestone_name}' not found. Use list_milestones to see available milestones."}
+            update_fields["milestone_id"] = ms.id
+        else:
+            update_fields["milestone_id"] = None
+
+    updated = await update_note(user_id, note.id, **update_fields)
+    if updated is None:
+        return {"success": False, "error": "Failed to update note."}
+
+    suggested = await suggest_tags(user_id, updated.title, updated.body or "")
+    schedule_embedding(updated.id, user_id, updated.title, updated.body or "")
+    item_type = "task" if updated.status is not None else "note"
+    return {
+        "success": True,
+        "type": "note_updated",
+        "updated": f"{item_type} '{updated.title}' (id: {updated.id})",
+        "data": {
+            "id": updated.id,
+            "title": updated.title,
+            "item_type": item_type,
+            "status": updated.status,
+            "tags": updated.tags or [],
+            "project_id": updated.project_id,
+        },
+        "suggested_tags": suggested,
+    }
+
+
+@tool(
+    name="search_notes",
+    description="Find notes or tasks by meaning. Returns a ranked list of matches with short previews. Use this when looking for items on a topic but you don't know the exact title. For the full body of a known item, use read_note instead.",
+    parameters={
+        "query": {"type": "string", "description": "A natural-language description of what you're looking for — concepts, themes, topics, or keywords"},
+        "type": {"type": "string", "enum": ["note", "task"], "description": "Restrict results to only notes or only tasks. Omit to search both."},
+        "project": {"type": "string", "description": "Optional project name to restrict search to notes in that project"},
+    },
+    required=["query"],
+    read_only=True,
+    briefing=True,
+)
+async def search_notes_tool(*, user_id, arguments, **_ctx):
+    query = arguments.get("query", "")
+    type_filter = arguments.get("type")
+    project_name = arguments.get("project")
+
+    is_task: bool | None = None
+    if type_filter == "note":
+        is_task = False
+    elif type_filter == "task":
+        is_task = True
+
+    search_project_id: int | None = None
+    if project_name:
+        proj = await resolve_project(user_id, project_name)
+        if proj:
+            search_project_id = proj.id
+
+    results_map: dict[int, dict] = {}
+
+    try:
+        from fabledassistant.services.embeddings import semantic_search_notes as _ssn
+        sem_results = await _ssn(
+            user_id, query, limit=8, threshold=0.40,
+            project_id=search_project_id,
+        )
+        for score, n in sem_results:
+            n_is_task = n.status is not None
+            if is_task is True and not n_is_task:
+                continue
+            if is_task is False and n_is_task:
+                continue
+            results_map[n.id] = {
+                "id": n.id,
+                "title": n.title,
+                "type": "task" if n_is_task else "note",
+                "status": n.status,
+                "relevance": f"{round(score * 100)}%",
+                "preview": (n.body[:300] + "\u2026") if n.body and len(n.body) > 300 else (n.body or ""),
+            }
+        logger.debug("search_notes semantic: %d results for %r", len(results_map), query)
+    except Exception:
+        logger.debug("Semantic search unavailable in search_notes, using keyword only", exc_info=True)
+
+    try:
+        kw_notes, _ = await list_notes(
+            user_id=user_id, q=query, is_task=is_task,
+            project_id=search_project_id, limit=8,
+        )
+        for n in kw_notes:
+            if n.id not in results_map:
+                results_map[n.id] = {
+                    "id": n.id,
+                    "title": n.title,
+                    "type": "task" if n.status is not None else "note",
+                    "status": n.status,
+                    "relevance": "keyword",
+                    "preview": (n.body[:300] + "\u2026") if n.body and len(n.body) > 300 else (n.body or ""),
+                }
+    except Exception:
+        logger.warning("Keyword fallback in search_notes failed", exc_info=True)
+
+    results = list(results_map.values())[:8]
+    return {
+        "success": True,
+        "type": "search",
+        "data": {"query": query, "count": len(results), "results": results},
+    }
+
+
+@tool(
+    name="read_note",
+    description="Read the full content of one specific note or task. Use when you know which item you want and need its complete body text. For discovering items by topic, use search_notes instead.",
+    parameters={
+        "query": {"type": "string", "description": "Title or keyword to identify the note or task"},
+    },
+    required=["query"],
+    read_only=True,
+    briefing=True,
+)
+async def get_note_tool(*, user_id, arguments, **_ctx):
+    query = arguments.get("query", "")
+    note = await get_note_by_title(user_id, query)
+    if note is None:
+        notes, _ = await list_notes(user_id=user_id, q=query, limit=3)
+        if not notes:
+            return {"success": False, "error": f"No note found matching '{query}'."}
+        note = notes[0]
+    return {
+        "success": True,
+        "type": "note_content",
+        "data": {"id": note.id, "title": note.title, "body": note.body or "", "tags": note.tags or []},
+    }
+
+
+@tool(
+    name="list_notes",
+    description="Browse recent notes (not tasks) with optional filters. Returns titles and short previews. Use when the user asks 'show my notes' or wants to browse by tag, project, or recency. For tasks use list_tasks; for topic search use search_notes.",
+    parameters={
+        "q": {"type": "string", "description": "Optional keyword filter"},
+        "tags": {"type": "array", "items": {"type": "string"}, "description": "Filter notes that have ALL of these tags"},
+        "project": {"type": "string", "description": "Filter notes by project name"},
+        "sort": {"type": "string", "enum": ["updated_at", "created_at", "title"], "description": "Sort field (default: updated_at)"},
+        "limit": {"type": "integer", "description": "Maximum number of notes to return (default 10)"},
+    },
+    read_only=True,
+    briefing=True,
+)
+async def list_notes_tool(*, user_id, arguments, **_ctx):
+    tags_raw = arguments.get("tags")
+    tags = tags_raw if isinstance(tags_raw, list) else None
+    project_id = None
+    project_name = arguments.get("project")
+    if project_name:
+        proj = await resolve_project(user_id, project_name)
+        if proj is None:
+            return {"success": False, "error": f"Project '{project_name}' not found."}
+        project_id = proj.id
+    notes, total = await list_notes(
+        user_id=user_id,
+        q=arguments.get("q") or arguments.get("query"),
+        tags=tags,
+        is_task=False,
+        project_id=project_id,
+        sort=arguments.get("sort", "updated_at"),
+        order="desc",
+        limit=int(arguments.get("limit", 10)),
+    )
+    results = [
+        {
+            "id": n.id,
+            "title": n.title,
+            "tags": n.tags or [],
+            "updated_at": n.updated_at.isoformat(),
+            "preview": (n.body[:200] + "...") if n.body and len(n.body) > 200 else (n.body or ""),
+        }
+        for n in notes
+    ]
+    return {
+        "success": True,
+        "type": "notes_list",
+        "data": {"total": total, "count": len(results), "results": results},
+    }
+
+
+@tool(
+    name="delete_note",
+    description="Delete any item from the user's knowledge base permanently — notes, tasks, persons (created via save_person), places (created via save_place), and lists (created via create_list) are all stored as notes and use this single delete tool. Use ONLY when the user explicitly asks to delete or remove an item. Always confirm with the user first — this cannot be undone.",
+    parameters={
+        "query": {"type": "string", "description": "Title or keyword to find the item to delete (works for notes, tasks, persons, places, and lists)"},
+        "confirmed": {"type": "boolean", "description": "Must be true — only set after the user has explicitly confirmed they want this item deleted."},
+    },
+    required=["query"],
+)
+async def delete_note_tool(*, user_id, arguments, **_ctx):
+    query = arguments.get("query", "")
+    note = await get_note_by_title(user_id, query)
+    if note is None:
+        notes, _ = await list_notes(user_id=user_id, q=query, limit=3)
+        if not notes:
+            return {"success": False, "error": f"No note or task found matching '{query}'."}
+        note = notes[0]
+    if not arguments.get("confirmed"):
+        item_type = "task" if note.status is not None else "note"
+        return {
+            "success": False,
+            "requires_confirmation": True,
+            "error": f"Deleting {item_type} '{note.title}' is permanent. Ask the user to confirm, then retry with confirmed=true.",
+        }
+    deleted = await delete_note(user_id, note.id)
+    if not deleted:
+        return {"success": False, "error": "Failed to delete."}
+    item_type = "task" if note.status is not None else "note"
+    return {"success": True, "type": f"{item_type}_deleted", "data": {"id": note.id, "title": note.title}}

src/fabledassistant/services/tools/profile.py

@@ -0,0 +1,77 @@
+"""User profile tools."""
+
+from __future__ import annotations
+
+from fabledassistant.services.tools._registry import tool
+
+
+@tool(
+    name="get_profile",
+    description=(
+        "Retrieve the user's stored profile: name, job title, industry, expertise level, "
+        "preferred response style, tone, and interests. Use this when you need to personalise "
+        "a response and the user's profile hasn't already been injected into context."
+    ),
+    parameters={},
+    read_only=True,
+)
+async def get_profile_tool(*, user_id, arguments, **_ctx):
+    from fabledassistant.services.user_profile import get_profile as _get_profile
+
+    profile = await _get_profile(user_id)
+    return {
+        "success": True,
+        "type": "profile",
+        "data": {
+            "display_name": profile.display_name or "",
+            "job_title": profile.job_title or "",
+            "industry": profile.industry or "",
+            "expertise_level": profile.expertise_level or "intermediate",
+            "response_style": profile.response_style or "balanced",
+            "tone": profile.tone or "casual",
+            "interests": profile.interests or [],
+        },
+    }
+
+
+@tool(
+    name="update_profile",
+    description=(
+        "Update the user's stored profile when they share personal information: their name, "
+        "job, industry, expertise, preferred response style, tone, or interests. "
+        "Only set fields the user has explicitly mentioned."
+    ),
+    parameters={
+        "display_name": {"type": "string", "description": "User's preferred display name"},
+        "job_title": {"type": "string", "description": "Job title (e.g. 'Software Engineer')"},
+        "industry": {"type": "string", "description": "Industry (e.g. 'Healthcare', 'Finance')"},
+        "expertise_level": {"type": "string", "enum": ["novice", "intermediate", "expert"], "description": "User's general expertise level"},
+        "response_style": {"type": "string", "enum": ["concise", "balanced", "detailed"], "description": "Preferred response length/depth"},
+        "tone": {"type": "string", "enum": ["casual", "professional", "technical"], "description": "Preferred communication tone"},
+        "interests": {"type": "array", "items": {"type": "string"}, "description": "List of topics or hobbies the user is interested in"},
+    },
+)
+async def update_profile_tool(*, user_id, arguments, **_ctx):
+    from fabledassistant.services.user_profile import VALID_EXPERTISE, VALID_STYLES, VALID_TONES, update_profile as _update_profile
+
+    data: dict = {}
+    for field in ("display_name", "job_title", "industry"):
+        val = arguments.get(field)
+        if val is not None:
+            data[field] = str(val)
+    expertise = arguments.get("expertise_level")
+    if expertise in VALID_EXPERTISE:
+        data["expertise_level"] = expertise
+    style = arguments.get("response_style")
+    if style in VALID_STYLES:
+        data["response_style"] = style
+    tone = arguments.get("tone")
+    if tone in VALID_TONES:
+        data["tone"] = tone
+    interests = arguments.get("interests")
+    if isinstance(interests, list):
+        data["interests"] = [str(i) for i in interests if str(i).strip()]
+    if not data:
+        return {"success": False, "error": "No valid fields provided to update."}
+    await _update_profile(user_id, data)
+    return {"success": True, "type": "profile_updated", "data": {"fields_updated": list(data.keys())}}

src/fabledassistant/services/tools/projects.py

@@ -0,0 +1,258 @@
+"""Project and milestone tools."""
+
+from __future__ import annotations
+
+from fabledassistant.services.tools._helpers import fuzzy_title_match, resolve_project
+from fabledassistant.services.tools._registry import tool
+
+
+@tool(
+    name="create_project",
+    description="Create a new project. Use list_projects first to check for duplicates. Only call after user has explicitly confirmed.",
+    parameters={
+        "title": {"type": "string", "description": "Project title"},
+        "description": {"type": "string", "description": "Brief description"},
+        "goal": {"type": "string", "description": "The goal or desired outcome"},
+        "color": {"type": "string", "description": "Optional hex color for the project (e.g. '#6366f1')"},
+        "confirmed": {"type": "boolean", "description": "Must be true — only set after the user has explicitly confirmed they want this project created."},
+    },
+    required=["title", "confirmed"],
+)
+async def create_project_tool(*, user_id, arguments, **_ctx):
+    from fabledassistant.services.projects import create_project as _create_project, get_project_by_title as _gpbt, list_projects as _lp
+
+    proj_title = arguments.get("title", "New Project")
+    existing_proj = await _gpbt(user_id, proj_title)
+    if existing_proj is not None:
+        return {"success": False, "error": f"A project titled '{existing_proj.title}' already exists (id: {existing_proj.id}). Use that project instead of creating a duplicate."}
+    all_projects = await _lp(user_id)
+    near_proj, ratio = fuzzy_title_match(proj_title, all_projects, threshold=0.55)
+    if near_proj is not None:
+        return {"success": False, "requires_confirmation": True, "error": f"A project with a similar name '{near_proj.title}' already exists (id: {near_proj.id}, similarity: {ratio:.0%}). This is likely the project you meant — use that project's name instead. Only call create_project again if you are certain this should be a completely new, separate project with a different confirmed=true."}
+    if not arguments.get("confirmed"):
+        return {"success": False, "requires_confirmation": True, "error": f"Project creation requires explicit user confirmation. Ask the user: 'Shall I create a new project titled \"{proj_title}\"?' Then retry with confirmed=true if they agree."}
+    project = await _create_project(
+        user_id,
+        title=proj_title,
+        description=arguments.get("description", ""),
+        goal=arguments.get("goal", ""),
+        color=arguments.get("color") or None,
+    )
+    return {"success": True, "type": "project", "data": project.to_dict()}
+
+
+@tool(
+    name="list_projects",
+    description="List the user's projects. Use when asked about projects, initiatives, or campaigns.",
+    parameters={
+        "status": {"type": "string", "enum": ["active", "completed", "archived"], "description": "Filter by status (omit for all)"},
+    },
+    read_only=True,
+    briefing=True,
+)
+async def list_projects_tool(*, user_id, arguments, **_ctx):
+    from fabledassistant.services.projects import list_projects as _list_projects
+
+    projects = await _list_projects(user_id, status=arguments.get("status"))
+    return {
+        "success": True,
+        "type": "projects_list",
+        "data": {"count": len(projects), "projects": [p.to_dict() for p in projects]},
+    }
+
+
+@tool(
+    name="get_project",
+    description="Get details and summary of a specific project.",
+    parameters={
+        "query": {"type": "string", "description": "Project name or keyword to find it"},
+    },
+    required=["query"],
+    read_only=True,
+    briefing=True,
+)
+async def get_project_tool(*, user_id, arguments, **_ctx):
+    from fabledassistant.services.projects import get_project_by_title as _gpbt, get_project_summary as _gps, list_projects as _lp
+
+    query = arguments.get("query", "")
+    project = await _gpbt(user_id, query)
+    if project is None:
+        all_projects = await _lp(user_id)
+        matches = [p for p in all_projects if query.lower() in p.title.lower()]
+        if matches:
+            project = matches[0]
+    if project is None:
+        return {"success": False, "error": f"No project found matching '{query}'"}
+    summary = await _gps(user_id, project.id)
+    data = project.to_dict()
+    data["summary"] = summary
+    return {"success": True, "type": "project_detail", "data": data}
+
+
+@tool(
+    name="update_project",
+    description="Update a project's title, status, description, goal, or color.",
+    parameters={
+        "query": {"type": "string", "description": "Project name to find"},
+        "title": {"type": "string", "description": "New project title"},
+        "status": {"type": "string", "enum": ["active", "completed", "archived"]},
+        "description": {"type": "string"},
+        "goal": {"type": "string"},
+        "color": {"type": "string", "description": "Hex color (e.g. '#6366f1'), or empty string to clear"},
+    },
+    required=["query"],
+)
+async def update_project_tool(*, user_id, arguments, **_ctx):
+    from fabledassistant.services.projects import get_project_by_title as _gpbt, update_project as _up, list_projects as _lp
+
+    query = arguments.get("query", "")
+    project = await _gpbt(user_id, query)
+    if project is None:
+        all_projects = await _lp(user_id)
+        matches = [p for p in all_projects if query.lower() in p.title.lower()]
+        if matches:
+            project = matches[0]
+    if project is None:
+        return {"success": False, "error": f"No project found matching '{query}'"}
+    fields = {}
+    for k in ("title", "status", "description", "goal"):
+        if k in arguments:
+            fields[k] = arguments[k]
+    if "color" in arguments:
+        fields["color"] = arguments["color"] or None
+    updated = await _up(user_id, project.id, **fields)
+    return {"success": True, "type": "project_updated", "data": updated.to_dict()}
+
+
+@tool(
+    name="search_projects",
+    description="Search for projects by name, description, or theme. Use this to find which project a user is referring to and get its ID for set_rag_scope.",
+    parameters={
+        "query": {"type": "string", "description": "Search query — project name, topic, or theme"},
+    },
+    required=["query"],
+    read_only=True,
+    briefing=True,
+)
+async def search_projects_tool(*, user_id, arguments, **_ctx):
+    from difflib import SequenceMatcher
+
+    from fabledassistant.services.projects import list_projects
+
+    query = str(arguments.get("query", "")).lower()
+    projects = await list_projects(user_id)
+    scored: list[tuple[float, object]] = []
+    for p in projects:
+        combined = f"{p.title} {p.description or ''} {p.auto_summary or ''}".lower()
+        base_score = SequenceMatcher(None, query, combined).ratio()
+        query_words = set(query.split())
+        overlap = sum(1 for w in query_words if w in combined)
+        score = base_score + overlap * 0.05
+        scored.append((score, p))
+    scored.sort(key=lambda x: x[0], reverse=True)
+    results = []
+    for score, p in scored[:5]:
+        results.append({
+            "id": p.id,
+            "title": p.title,
+            "summary_snippet": (p.auto_summary or p.description or "")[:200],
+            "score": round(score, 3),
+        })
+    return {"type": "projects_list", "data": {"projects": results}}
+
+
+@tool(
+    name="create_milestone",
+    description="Create a milestone inside a project. Confirm name and project with user before calling.",
+    parameters={
+        "project": {"type": "string", "description": "Project title"},
+        "title": {"type": "string", "description": "Milestone title"},
+        "description": {"type": "string", "description": "Optional description"},
+        "confirmed": {"type": "boolean", "description": "Must be true — only set after the user has explicitly confirmed they want this milestone created."},
+    },
+    required=["project", "title", "confirmed"],
+)
+async def create_milestone_tool(*, user_id, arguments, **_ctx):
+    from fabledassistant.services.milestones import create_milestone as _cm, get_milestone_by_title as _gmbt2, list_milestones as _lms
+
+    project_name = arguments.get("project", "")
+    ms_title = arguments.get("title", "Untitled Milestone")
+    if not project_name:
+        return {"success": False, "error": "project is required"}
+    if not arguments.get("confirmed"):
+        return {"success": False, "requires_confirmation": True, "error": f"Milestone creation requires explicit user confirmation. Ask the user: 'Shall I create a milestone titled \"{ms_title}\" in project \"{project_name}\"?' Then retry with confirmed=true if they agree."}
+    proj = await resolve_project(user_id, project_name)
+    if proj is None:
+        return {"success": False, "error": f"Project '{project_name}' not found. Use list_projects to find the correct project name and retry with the exact name."}
+    existing_ms = await _gmbt2(user_id, proj.id, ms_title)
+    if existing_ms is not None:
+        return {"success": False, "error": f"A milestone titled '{existing_ms.title}' already exists in '{proj.title}' (id: {existing_ms.id}). Use update_milestone to modify it instead."}
+    all_ms = await _lms(user_id, proj.id)
+    near_ms, ratio = fuzzy_title_match(ms_title, all_ms)
+    if near_ms is not None:
+        return {"success": False, "error": f"A milestone with a very similar title '{near_ms.title}' already exists in '{proj.title}' (id: {near_ms.id}, similarity: {ratio:.0%}). Use update_milestone to modify it instead."}
+    ms = await _cm(user_id, proj.id, title=ms_title, description=arguments.get("description"))
+    return {"success": True, "type": "milestone", "data": ms.to_dict()}
+
+
+@tool(
+    name="update_milestone",
+    description="Update the title, description, or status of an existing milestone.",
+    parameters={
+        "project": {"type": "string", "description": "Project title the milestone belongs to"},
+        "milestone": {"type": "string", "description": "Current milestone title to look up"},
+        "title": {"type": "string", "description": "New title (omit to keep current)"},
+        "description": {"type": "string", "description": "New description (omit to keep current)"},
+        "status": {"type": "string", "enum": ["active", "done"], "description": "New status: 'active' (in progress) or 'done' (completed)"},
+    },
+    required=["project", "milestone"],
+)
+async def update_milestone_tool(*, user_id, arguments, **_ctx):
+    from fabledassistant.services.milestones import get_milestone_by_title as _gmbt, update_milestone as _um
+
+    project_name = arguments.get("project", "")
+    milestone_name = arguments.get("milestone", "")
+    if not project_name or not milestone_name:
+        return {"success": False, "error": "Both project and milestone are required"}
+    proj = await resolve_project(user_id, project_name)
+    if proj is None:
+        return {"success": False, "error": f"Project '{project_name}' not found"}
+    ms = await _gmbt(user_id, proj.id, milestone_name)
+    if ms is None:
+        return {"success": False, "error": f"Milestone '{milestone_name}' not found in project '{proj.title}'. Use list_milestones to see available milestones."}
+    fields: dict[str, object] = {}
+    if "title" in arguments:
+        fields["title"] = arguments["title"]
+    if "description" in arguments:
+        fields["description"] = arguments["description"]
+    if "status" in arguments:
+        fields["status"] = arguments["status"]
+    if not fields:
+        return {"success": False, "error": "No fields to update — provide at least one of: title, description, status"}
+    updated = await _um(user_id, ms.id, **fields)
+    return {"success": True, "type": "milestone", "data": updated.to_dict()}
+
+
+@tool(
+    name="list_milestones",
+    description="List milestones for a project with their progress percentages.",
+    parameters={
+        "project": {"type": "string", "description": "Project name to look up"},
+    },
+    required=["project"],
+    read_only=True,
+    briefing=True,
+)
+async def list_milestones_tool(*, user_id, arguments, **_ctx):
+    from fabledassistant.services.milestones import get_project_milestone_summary
+
+    project_name = arguments.get("project", "")
+    proj = await resolve_project(user_id, project_name)
+    if proj is None:
+        return {"success": False, "error": f"No project found matching '{project_name}'"}
+    summary = await get_project_milestone_summary(user_id, proj.id)
+    return {
+        "success": True,
+        "type": "milestones_list",
+        "data": {"project": proj.title, "count": len(summary), "milestones": summary},
+    }

src/fabledassistant/services/tools/rag.py

@@ -0,0 +1,34 @@
+"""RAG scope tool."""
+
+from __future__ import annotations
+
+from fabledassistant.services.tools._registry import tool
+
+
+@tool(
+    name="set_rag_scope",
+    description="Change the RAG scope for this conversation. Use project_id=<int> to scope to a project, project_id=null to scope to orphan notes only, project_id=-1 for all notes.",
+    parameters={
+        "project_id": {"type": ["integer", "null"], "description": "Project ID to scope to, null for orphan-only, -1 for all notes"},
+    },
+    required=["project_id"],
+)
+async def set_rag_scope_tool(*, user_id, arguments, conv_id=None, workspace_project_id=None, **_ctx):
+    if workspace_project_id is not None:
+        return {"success": False, "error": "Cannot change RAG scope in workspace view"}
+    if conv_id is None:
+        return {"success": False, "error": "No conversation context available"}
+    project_id = arguments.get("project_id")
+    if project_id is not None and project_id != -1:
+        from fabledassistant.services.projects import get_project
+        proj = await get_project(user_id, int(project_id))
+        if proj is None:
+            return {"success": False, "error": "Project not found"}
+        scope_label = proj.title
+    elif project_id == -1:
+        scope_label = "All notes"
+    else:
+        scope_label = "Orphan notes only"
+    from fabledassistant.services.chat import update_conversation
+    await update_conversation(user_id, conv_id, rag_project_id=project_id)
+    return {"success": True, "type": "rag_scope_set", "scope_label": scope_label}

src/fabledassistant/services/tools/rss.py

@@ -0,0 +1,99 @@
+"""RSS and article tools."""
+
+from __future__ import annotations
+
+import logging
+
+from fabledassistant.services.tools._registry import tool
+
+logger = logging.getLogger(__name__)
+
+
+@tool(
+    name="get_rss_items",
+    description="Get recent items from the user's RSS feeds (news, blogs, Reddit, podcasts). Returns titles, URLs, and summaries of recent posts.",
+    parameters={
+        "limit": {"type": "integer", "description": "Number of items to return (default 15, max 50)"},
+        "category": {"type": "string", "description": "Filter by feed category (e.g. 'news', 'tech'). Omit for all."},
+    },
+    read_only=True,
+    briefing=True,
+)
+async def get_rss_items_tool(*, user_id, arguments, **_ctx):
+    from fabledassistant.services.rss import get_recent_items
+
+    limit = min(int(arguments.get("limit", 15)), 50)
+    items = await get_recent_items(user_id, limit=limit)
+    return {"data": {"items": items, "count": len(items)}}
+
+
+@tool(
+    name="add_rss_feed",
+    description="Add an RSS/Atom feed. Use when user asks to subscribe to or track a feed, blog, subreddit, or podcast.",
+    parameters={
+        "url": {"type": "string", "description": "The RSS/Atom feed URL to add."},
+        "category": {"type": "string", "description": "Optional category label (e.g. 'news', 'tech', 'reddit'). Omit if unsure."},
+    },
+    required=["url"],
+)
+async def add_rss_feed_tool(*, user_id, arguments, **_ctx):
+    import asyncio as _asyncio
+
+    from sqlalchemy import select as _select
+
+    from fabledassistant.models import async_session as _async_session
+    from fabledassistant.models.rss_feed import RssFeed
+    from fabledassistant.services.rss import fetch_and_cache_feed
+
+    url = str(arguments.get("url", "")).strip()
+    if not url:
+        return {"error": "url is required"}
+    category = arguments.get("category") or None
+    async with _async_session() as session:
+        existing = await session.execute(
+            _select(RssFeed).where(RssFeed.user_id == user_id, RssFeed.url == url)
+        )
+        if existing.scalars().first():
+            return {"error": "Feed already added", "url": url}
+        feed = RssFeed(user_id=user_id, url=url, title="", category=category)
+        session.add(feed)
+        await session.commit()
+        await session.refresh(feed)
+        feed_id = feed.id
+    _asyncio.create_task(fetch_and_cache_feed(feed_id, url))
+    return {"data": {"id": feed_id, "url": url, "message": "Feed added and fetching started."}}
+
+
+@tool(
+    name="read_article",
+    description=(
+        "Fetch and read the full text of a web page or article from a URL. "
+        "Use when the user shares a URL and wants you to read it, "
+        "or to get the full content of a linked page. "
+        "Do NOT use search_web for URLs — use this tool instead."
+    ),
+    parameters={
+        "url": {"type": "string", "description": "The URL to fetch and read"},
+    },
+    required=["url"],
+    read_only=True,
+    briefing=True,
+)
+async def read_article_tool(*, user_id, arguments, **_ctx):
+    from fabledassistant.services.rss import _fetch_full_article
+
+    url = arguments.get("url", "").strip()
+    if not url:
+        return {"success": False, "error": "No URL provided"}
+    content = await _fetch_full_article(url)
+    if not content:
+        return {"success": False, "error": f"Could not fetch article content from {url}"}
+    _TOOL_CONTENT_CAP = 40_000
+    truncated = len(content) > _TOOL_CONTENT_CAP
+    return {
+        "success": True,
+        "type": "article_content",
+        "url": url,
+        "content": content[:_TOOL_CONTENT_CAP],
+        "truncated": truncated,
+    }

src/fabledassistant/services/tools/tasks.py

@@ -0,0 +1,116 @@
+"""Task tools: list, log work."""
+
+from __future__ import annotations
+
+from fabledassistant.services.notes import get_note_by_title, list_notes
+from fabledassistant.services.tools._helpers import (
+    parse_due_date,
+    resolve_project,
+)
+from fabledassistant.services.tools._registry import tool
+
+
+@tool(
+    name="list_tasks",
+    description="List tasks with optional filters. For overdue tasks, set due_before to today's date.",
+    parameters={
+        "q": {"type": "string", "description": "Optional keyword filter — searches title and body"},
+        "status": {"type": "array", "items": {"type": "string", "enum": ["todo", "in_progress", "done", "cancelled"]}, "description": "Filter by one or more statuses. Omit for all."},
+        "priority": {"type": "string", "enum": ["none", "low", "medium", "high"], "description": "Filter by priority level"},
+        "due_before": {"type": "string", "description": "Return tasks due before this date (YYYY-MM-DD). Use today's date to find overdue tasks."},
+        "due_after": {"type": "string", "description": "Return tasks due on or after this date (YYYY-MM-DD)"},
+        "project": {"type": "string", "description": "Filter tasks by project name"},
+        "milestone": {"type": "string", "description": "Filter tasks by milestone name within a project"},
+        "limit": {"type": "integer", "description": "Maximum number of tasks to return (default 10)"},
+    },
+    read_only=True,
+    briefing=True,
+)
+async def list_tasks_tool(*, user_id, arguments, **_ctx):
+    project_id = None
+    milestone_id = None
+    project_name = arguments.get("project")
+    milestone_name = arguments.get("milestone")
+    if project_name:
+        proj = await resolve_project(user_id, project_name)
+        if proj:
+            project_id = proj.id
+            if milestone_name:
+                from fabledassistant.services.milestones import get_milestone_by_title
+                ms = await get_milestone_by_title(user_id, proj.id, milestone_name)
+                if ms:
+                    milestone_id = ms.id
+    elif milestone_name:
+        from fabledassistant.services.milestones import find_milestone_by_title
+        ms = await find_milestone_by_title(user_id, milestone_name)
+        if ms:
+            milestone_id = ms.id
+    notes, total = await list_notes(
+        user_id=user_id,
+        q=arguments.get("q") or None,
+        is_task=True,
+        status=arguments.get("status"),
+        priority=arguments.get("priority"),
+        due_before=parse_due_date(arguments.get("due_before")),
+        due_after=parse_due_date(arguments.get("due_after")),
+        project_id=project_id,
+        milestone_id=milestone_id,
+        exclude_paused_projects=project_id is None,
+        limit=int(arguments.get("limit", 10)),
+        sort="due_date",
+        order="asc",
+    )
+    results = []
+    for n in notes:
+        results.append({
+            "id": n.id,
+            "title": n.title,
+            "status": n.status,
+            "priority": n.priority,
+            "due_date": str(n.due_date) if n.due_date else None,
+            "project_id": n.project_id,
+            "preview": (n.body[:120] + "...") if n.body and len(n.body) > 120 else (n.body or ""),
+        })
+    return {
+        "success": True,
+        "type": "tasks",
+        "data": {"total": total, "count": len(results), "results": results},
+    }
+
+
+@tool(
+    name="log_work",
+    description="Add a work log entry to a task to record progress, work done, or time spent. Use this when the user says they worked on, completed, or spent time on a task.",
+    parameters={
+        "task": {"type": "string", "description": "Title or keyword identifying the task (required)"},
+        "content": {"type": "string", "description": "Description of the work done (required)"},
+        "duration_minutes": {"type": "integer", "description": "Optional time spent in minutes"},
+    },
+    required=["task", "content"],
+)
+async def log_work_tool(*, user_id, arguments, **_ctx):
+    from fabledassistant.services.task_logs import create_log as _create_log
+
+    task_query = arguments.get("task", "")
+    content = arguments.get("content", "").strip()
+    if not task_query:
+        return {"success": False, "error": "task is required"}
+    if not content:
+        return {"success": False, "error": "content is required"}
+    duration_minutes = arguments.get("duration_minutes")
+    if duration_minutes is not None:
+        try:
+            duration_minutes = int(duration_minutes)
+        except (TypeError, ValueError):
+            duration_minutes = None
+    note = await get_note_by_title(user_id, task_query)
+    if note is None or note.status is None:
+        notes, _ = await list_notes(user_id=user_id, q=task_query, is_task=True, limit=5)
+        note = notes[0] if notes else None
+    if note is None:
+        return {"success": False, "error": f"No task found matching '{task_query}'."}
+    try:
+        log = await _create_log(user_id, note.id, content, duration_minutes)
+    except ValueError as e:
+        return {"success": False, "error": str(e)}
+    return {"success": True, "log": log.to_dict(), "task": note.title}

src/fabledassistant/services/tools/utility.py

@@ -0,0 +1,34 @@
+"""General-purpose utility tools."""
+
+from __future__ import annotations
+
+from fabledassistant.services.tools._registry import tool
+
+
+@tool(
+    name="calculate",
+    description=(
+        "Evaluate a mathematical expression and return the exact result. Use this for any "
+        "arithmetic, percentages, unit conversions, or multi-step calculations where precision "
+        "matters. Supports standard math operators (+, -, *, /, **, %) and all Python math "
+        "module functions (sqrt, log, sin, cos, floor, ceil, etc.)."
+    ),
+    parameters={
+        "expression": {"type": "string", "description": "A valid Python math expression (e.g. '(450 * 1.13) / 12', 'sqrt(144)', 'log(1000, 10)')"},
+    },
+    required=["expression"],
+)
+async def calculate_tool(*, user_id, arguments, **_ctx):
+    import math as _math
+
+    expr = str(arguments.get("expression", "")).strip()
+    if not expr:
+        return {"success": False, "error": "expression is required"}
+    allowed_names = {k: v for k, v in vars(_math).items() if not k.startswith("_")}
+    allowed_names["abs"] = abs
+    allowed_names["round"] = round
+    try:
+        result = eval(expr, {"__builtins__": {}}, allowed_names)  # noqa: S307
+    except Exception as calc_err:
+        return {"success": False, "error": f"Could not evaluate expression: {calc_err}"}
+    return {"success": True, "type": "calculation", "data": {"expression": expr, "result": result}}

src/fabledassistant/services/tools/weather.py

@@ -0,0 +1,122 @@
+"""Weather tool."""
+
+from __future__ import annotations
+
+import json as _wx_json
+import logging
+from datetime import datetime, timedelta, timezone
+
+from fabledassistant.services.tools._registry import tool
+
+logger = logging.getLogger(__name__)
+
+
+@tool(
+    name="get_weather",
+    description=(
+        "Get the weather forecast. By default returns today only — use days=7 when the user "
+        "explicitly asks for a multi-day forecast or 'this week'. "
+        "Pass a city/region name to look up any location, or omit to get the user's configured home/work locations."
+    ),
+    parameters={
+        "location": {"type": "string", "description": "City/region to look up, or 'home'/'work' for saved locations. Omit for all saved."},
+        "days": {"type": "integer", "description": "Number of days to return (1–8). Default 1 (today only). Use 7 or 8 when the user asks for a weekly forecast or multiple days."},
+    },
+    read_only=True,
+    briefing=True,
+)
+async def get_weather_tool(*, user_id, arguments, **_ctx):
+    from fabledassistant.services.briefing_pipeline import _get_temp_unit
+    from fabledassistant.services.weather import (
+        _fetch_open_meteo,
+        geocode,
+        get_cached_weather,
+        parse_forecast,
+        refresh_location_cache,
+    )
+
+    location = (arguments.get("location") or "").strip()
+    num_days = max(1, min(8, int(arguments.get("days") or 1)))
+    temp_unit = await _get_temp_unit(user_id)
+
+    def _apply_unit(days: list[dict]) -> list[dict]:
+        if temp_unit != "F":
+            return days
+        def _c_to_f(v: float | None) -> float | None:
+            return round(v * 9 / 5 + 32, 1) if v is not None else None
+        return [
+            {**d, "temp_max": _c_to_f(d.get("temp_max")), "temp_min": _c_to_f(d.get("temp_min"))}
+            for d in days
+        ]
+
+    _known = {"home", "work", "all", ""}
+    if location.lower() not in _known:
+        try:
+            lat, lon, label = await geocode(location)
+            raw = await _fetch_open_meteo(lat, lon)
+            days = _apply_unit(parse_forecast(raw))[:num_days]
+            return {"success": True, "data": {"locations": [{
+                "location_key": "query",
+                "location_label": label,
+                "fetched_at": datetime.now(timezone.utc).isoformat(),
+                "days": days,
+                "temp_unit": temp_unit,
+                "changes_since_last_fetch": [],
+            }]}}
+        except Exception as e:
+            return {"success": False, "error": f"Could not get weather for '{location}': {e}"}
+
+    locations = await get_cached_weather(user_id)
+    if location.lower() not in ("all", ""):
+        locations = [loc for loc in locations if loc["location_key"] == location.lower()]
+    now_utc = datetime.now(timezone.utc)
+    stale_cutoff = now_utc - timedelta(hours=6)
+    stale_keys = []
+    for loc in locations:
+        try:
+            fetched = datetime.fromisoformat(loc.get("fetched_at", ""))
+        except Exception:
+            fetched = None
+        if fetched is None or fetched < stale_cutoff:
+            stale_keys.append(loc["location_key"])
+    if stale_keys:
+        try:
+            from fabledassistant.services.settings import get_setting as _wx_get_setting
+            raw_cfg = await _wx_get_setting(user_id, "briefing_config", "{}")
+            cfg = _wx_json.loads(raw_cfg) if isinstance(raw_cfg, str) else (raw_cfg or {})
+            cfg_locs = cfg.get("locations", {}) if isinstance(cfg, dict) else {}
+            for key in stale_keys:
+                meta = cfg_locs.get(key) or {}
+                if meta.get("lat") and meta.get("lon"):
+                    try:
+                        await refresh_location_cache(
+                            user_id=user_id,
+                            location_key=key,
+                            location_label=meta.get("label", key),
+                            lat=meta["lat"],
+                            lon=meta["lon"],
+                        )
+                    except Exception:
+                        logger.debug("Weather refresh failed for %s", key, exc_info=True)
+            locations = await get_cached_weather(user_id)
+            if location.lower() not in ("all", ""):
+                locations = [loc for loc in locations if loc["location_key"] == location.lower()]
+        except Exception:
+            logger.debug("Weather staleness refresh failed", exc_info=True)
+
+    stamped_locations = []
+    for loc in locations:
+        days = _apply_unit(loc.get("days", []))[:num_days]
+        try:
+            fetched = datetime.fromisoformat(loc.get("fetched_at", ""))
+            age_h = (now_utc - fetched).total_seconds() / 3600.0
+        except Exception:
+            age_h = None
+        stamped_locations.append({
+            **loc,
+            "days": days,
+            "temp_unit": temp_unit,
+            "cache_age_hours": round(age_h, 1) if age_h is not None else None,
+            "is_stale": age_h is not None and age_h > 12.0,
+        })
+    return {"success": True, "data": {"locations": stamped_locations}}

src/fabledassistant/services/tools/web.py

@@ -0,0 +1,117 @@
+"""Web search, research, and image tools (require SearXNG)."""
+
+from __future__ import annotations
+
+import logging
+
+from fabledassistant.services.tools._registry import tool
+
+logger = logging.getLogger(__name__)
+
+
+@tool(
+    name="search_web",
+    description=(
+        "Quick web lookup — returns search result snippets for factual questions, current events, "
+        "definitions, or version numbers. No note is saved. Use research_topic when the user wants "
+        "a comprehensive written report saved as a note."
+    ),
+    parameters={
+        "query": {"type": "string", "description": "The search query"},
+    },
+    required=["query"],
+    requires="searxng",
+)
+async def search_web_tool(*, user_id, arguments, **_ctx):
+    from fabledassistant.services.research import _search_searxng
+
+    query = arguments.get("query", "")
+    results = await _search_searxng(query)
+    if not results:
+        return {"success": False, "error": f"No results found for '{query}'"}
+    return {
+        "success": True,
+        "type": "web_search",
+        "data": {"query": query, "results": results, "count": len(results)},
+    }
+
+
+@tool(
+    name="research_topic",
+    description=(
+        "Deep web research — searches multiple sources, synthesizes findings, and saves the result "
+        "as a structured note with sections and citations. Use when the user says 'research', "
+        "'look into', or wants a comprehensive write-up. Takes 30–120 seconds. "
+        "For a quick factual answer without saving a note, use search_web."
+    ),
+    parameters={
+        "topic": {"type": "string", "description": "The topic or question to research"},
+    },
+    required=["topic"],
+    requires="searxng",
+)
+async def research_topic_tool(*, user_id, arguments, **_ctx):
+    # Research is always intercepted in generation_task.py (round 0) before execute_tool.
+    # Reaching here indicates a code path regression.
+    topic = arguments.get("topic", "")
+    logger.error(
+        "research_topic reached execute_tool — should have been intercepted upstream "
+        "in generation_task.py. topic=%r",
+        topic,
+    )
+    return {"success": False, "error": "Research could not be started. Please try again."}
+
+
+@tool(
+    name="search_images",
+    description="Search and display images inline. Use ONLY when the user explicitly asks to see, show, or find an image or photo. Not for factual questions — use search_web for those.",
+    parameters={
+        "query": {"type": "string", "description": "The image search query"},
+    },
+    required=["query"],
+    requires="searxng",
+)
+async def search_images_tool(*, user_id, arguments, **_ctx):
+    from fabledassistant.services.images import fetch_and_store_image
+    from fabledassistant.services.research import _search_searxng_images
+
+    query = arguments.get("query", "")
+    if not query:
+        return {"success": False, "error": "query is required"}
+    raw_results = await _search_searxng_images(query)
+    if not raw_results:
+        return {"success": False, "error": f"No images found for '{query}'"}
+    images = []
+    for r in raw_results[:2]:
+        img_url = r.get("img_src", "")
+        if not img_url:
+            continue
+        record = await fetch_and_store_image(
+            url=img_url,
+            title=r.get("title"),
+            source_domain=r.get("source_domain"),
+        )
+        if record:
+            title = record.title or query
+            page_url = r.get("page_url", "")
+            source_domain = r.get("source_domain", "")
+            images.append({
+                "embed": f"![{title}](/api/images/{record.id})",
+                "citation": f"*Source: [{source_domain or 'image'}]({page_url})*",
+                "page_url": page_url,
+                "title": title,
+            })
+    if not images:
+        return {"success": False, "error": f"Could not retrieve images for '{query}'"}
+    return {
+        "success": True,
+        "type": "image_search",
+        "data": {
+            "query": query,
+            "images": images,
+            "instructions": (
+                "Embed each image in your response by writing the 'embed' field verbatim, "
+                "then the 'citation' field on the next line. Do not paraphrase or list URLs."
+            ),
+        },
+    }

src/fabledassistant/services/tz.py

@@ -0,0 +1,47 @@
+"""User-timezone helpers.
+
+All datetimes in the DB are stored as UTC. The helpers here bridge between
+that UTC storage and the user's configured local timezone (IANA string in
+the ``user_timezone`` setting). Use these anywhere the model or UI talks
+in terms of "today", "tomorrow", or a bare calendar date — never
+``date.today()`` or ``datetime.now(timezone.utc)`` inside a per-user flow.
+"""
+from __future__ import annotations
+
+from datetime import date, datetime, timedelta
+from zoneinfo import ZoneInfo, ZoneInfoNotFoundError
+
+from fabledassistant.services.settings import get_setting
+
+# Briefing day boundary — kept in sync with the compilation slot in
+# ``briefing_scheduler.SLOTS``. The briefing day flips at this local hour
+# (not midnight) so the 00:00–04:00 local window still shows yesterday's
+# briefing until the 4am compilation generates the new one.
+BRIEFING_DAY_START_HOUR = 4
+
+
+async def get_user_tz(user_id: int) -> ZoneInfo:
+    """Return the user's IANA ``ZoneInfo``, falling back to UTC."""
+    tz_str = await get_setting(user_id, "user_timezone") or "UTC"
+    try:
+        return ZoneInfo(tz_str)
+    except (ZoneInfoNotFoundError, KeyError):
+        return ZoneInfo("UTC")
+
+
+async def user_today(user_id: int) -> date:
+    """Return today's calendar date in the user's local timezone."""
+    tz = await get_user_tz(user_id)
+    return datetime.now(tz).date()
+
+
+async def user_briefing_date(user_id: int) -> date:
+    """Return the current "briefing day" in the user's local timezone.
+
+    The briefing day flips at ``BRIEFING_DAY_START_HOUR`` (4am local),
+    aligned with the compilation slot that generates the day's briefing.
+    Between 00:00 and 04:00 local this still returns *yesterday*, so the
+    UI keeps showing the in-progress briefing until the new one is built.
+    """
+    tz = await get_user_tz(user_id)
+    return (datetime.now(tz) - timedelta(hours=BRIEFING_DAY_START_HOUR)).date()

src/fabledassistant/services/user_profile.py

@@ -114,7 +114,7 @@ async def _consolidate_observations(user_id: int) -> str:
     learned_summary paragraph. Prunes raw entries older than 30 days afterwards.
     """
     from fabledassistant.config import Config
-    from fabledassistant.services.briefing_pipeline import _llm_synthesise
+    from fabledassistant.services.llm import generate_completion
     from fabledassistant.services.settings import get_setting
 
     async with async_session() as session:
@@ -145,7 +145,17 @@ async def _consolidate_observations(user_id: int) -> str:
     user_prompt += f"New observations:\n{obs_text}"
 
     model = await get_setting(user_id, "default_model", Config.OLLAMA_MODEL)
-    new_summary = await _llm_synthesise(system, user_prompt, model)
+    try:
+        new_summary = (await generate_completion(
+            [
+                {"role": "system", "content": system},
+                {"role": "user", "content": user_prompt},
+            ],
+            model,
+        )).strip()
+    except Exception:
+        logger.warning("Observation consolidation failed for user %d", user_id, exc_info=True)
+        new_summary = ""
 
     if new_summary:
         cutoff = (date.today() - timedelta(days=30)).isoformat()

tests/test_article_reading.py

@@ -134,3 +134,83 @@ def test_history_builder_no_tool_calls_unchanged():
     assert len(history) == 2
     assert history[0] == {"role": "user", "content": "Hello"}
     assert history[1] == {"role": "assistant", "content": "Hi there!"}
+
+
+# ---------------------------------------------------------------------------
+# prepare_article_context tests
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_prepare_article_context_small_passthrough():
+    """Articles under CHAR_BUDGET pass through unchanged with zero LLM calls."""
+    from fabledassistant.services import article_context
+
+    body = "A short article.\n\nWith two paragraphs."
+    with patch(
+        "fabledassistant.services.article_context.generate_completion",
+        new_callable=AsyncMock,
+    ) as mock_gen:
+        out = await article_context.prepare_article_context(
+            "Title", "https://example.com", body, "test-model",
+        )
+
+    assert out == body
+    mock_gen.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_prepare_article_context_large_runs_map_reduce():
+    """Articles over CHAR_BUDGET are chunked and map-reduced via the background model."""
+    from fabledassistant.services import article_context
+
+    # CHAR_BUDGET is 48_000 — build a body well over that with paragraph breaks
+    # so the chunker has natural splits to work with.
+    paragraph = ("Lorem ipsum dolor sit amet, consectetur adipiscing elit. " * 40).strip()
+    body = "\n\n".join([paragraph] * 30)  # ~70k+ chars across 30 paragraphs
+    assert len(body) > article_context.CHAR_BUDGET
+
+    with patch(
+        "fabledassistant.services.article_context.generate_completion",
+        new_callable=AsyncMock,
+        return_value="Summary of this section with specific claims preserved.",
+    ) as mock_gen:
+        out = await article_context.prepare_article_context(
+            "Long Article", "https://example.com/long", body, "test-model",
+        )
+
+    # At least one LLM call fired (the map step ran)
+    assert mock_gen.await_count >= 1
+    # Output carries the oversized-article header and section markers
+    assert "longer than the chat window" in out
+    assert "## Section 1" in out
+    # Map output is much smaller than the raw body
+    assert len(out) < len(body)
+
+
+def test_chunk_by_paragraph_respects_boundaries():
+    """Chunker splits on paragraph breaks, not mid-sentence."""
+    from fabledassistant.services.article_context import _chunk_by_paragraph, CHUNK_CHARS
+
+    paragraphs = [f"Paragraph {i}. " + ("x" * 1000) for i in range(20)]
+    body = "\n\n".join(paragraphs)
+
+    chunks = _chunk_by_paragraph(body)
+
+    # Each chunk stays under the budget
+    for chunk in chunks:
+        assert len(chunk) <= CHUNK_CHARS
+    # Total content is preserved (modulo overlap duplication, so ≥ original)
+    assert sum(len(c) for c in chunks) >= len(body) * 0.9
+
+
+def test_chunk_by_paragraph_handles_oversized_paragraph():
+    """A single paragraph larger than CHUNK_CHARS gets split mid-paragraph."""
+    from fabledassistant.services.article_context import _chunk_by_paragraph, CHUNK_CHARS
+
+    body = "x" * (CHUNK_CHARS * 3)  # one huge paragraph, no breaks
+    chunks = _chunk_by_paragraph(body)
+
+    assert len(chunks) >= 3
+    for chunk in chunks:
+        assert len(chunk) <= CHUNK_CHARS

tests/test_briefing_pipeline.py

@@ -12,52 +12,6 @@ async def test_get_or_create_profile_note_returns_body():
         assert body == ""
 
 
-def test_format_task_for_briefing():
-    """format_task() should return a compact single-line string."""
-    from fabledassistant.services.briefing_pipeline import format_task
-    task = {"title": "Write design doc", "status": "in_progress", "due_date": "2026-03-12", "priority": "high"}
-    result = format_task(task)
-    assert "Write design doc" in result
-    assert "in_progress" in result or "In Progress" in result
-
-
-
-def test_compute_task_snapshot_hash():
-    """compute_task_hash should return a stable SHA-256 hex string."""
-    from fabledassistant.services.briefing_pipeline import compute_task_hash
-
-    task = {"status": "todo", "priority": "high", "due_date": "2026-03-25", "title": "Write spec"}
-    h = compute_task_hash(task)
-    assert len(h) == 64  # SHA-256 hex
-    assert h == compute_task_hash(task)
-    assert h != compute_task_hash({**task, "status": "done"})
-
-
-@pytest.mark.asyncio
-async def test_split_changed_tasks_all_new():
-    """split_changed_tasks should return all tasks as changed when no snapshot exists."""
-    from fabledassistant.services.briefing_pipeline import split_changed_tasks
-
-    tasks = [
-        {"task_id": 1, "title": "A", "status": "todo", "priority": "none", "due_date": None},
-    ]
-
-    with patch(
-        "fabledassistant.services.briefing_pipeline.async_session"
-    ) as mock_cls:
-        mock_session = AsyncMock()
-        mock_session.__aenter__ = AsyncMock(return_value=mock_session)
-        mock_session.__aexit__ = AsyncMock(return_value=False)
-        mock_session.execute = AsyncMock(return_value=MagicMock(
-            scalars=MagicMock(return_value=MagicMock(all=MagicMock(return_value=[])))
-        ))
-        mock_cls.return_value = mock_session
-
-        changed, unchanged_count = await split_changed_tasks(user_id=1, tasks=tasks)
-        assert len(changed) == 1
-        assert unchanged_count == 0
-
-
 @pytest.mark.asyncio
 async def test_post_message_accepts_metadata():
     """post_message should accept an optional metadata dict and store it."""

tests/test_briefing_scheduler.py

@@ -84,7 +84,7 @@ async def test_run_slot_morning_runs_on_work_day():
          patch("fabledassistant.services.user_profile.get_profile", new_callable=AsyncMock, return_value=fake_profile), \
          patch("fabledassistant.services.settings.get_setting", new_callable=AsyncMock, return_value="UTC"), \
          patch("fabledassistant.services.briefing_conversations.get_or_create_today_conversation", new_callable=AsyncMock) as mock_conv, \
-         patch("fabledassistant.services.briefing_pipeline.run_slot_injection", new_callable=AsyncMock, return_value="") as mock_inject, \
+         patch("fabledassistant.services.briefing_pipeline.run_slot_injection", new_callable=AsyncMock, return_value=("", {})) as mock_inject, \
          patch("fabledassistant.services.briefing_conversations.post_message", new_callable=AsyncMock), \
          patch("fabledassistant.services.push.send_push_notification", new_callable=AsyncMock):
 

tests/test_calendar_tool_tz.py

@@ -0,0 +1,134 @@
+"""Regression tests: calendar tool respects the user's configured timezone."""
+
+from datetime import timezone
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_create_event_all_day_bare_date_interprets_local_midnight():
+    """Bare date like '2026-09-30' for a NY user = 2026-09-30 00:00 NY,
+    stored as 2026-09-30 04:00 UTC — NOT 2026-09-30 00:00 UTC (which would
+    be 2026-09-29 19:00 NY and land on the wrong day)."""
+    from fabledassistant.services.tools.calendar import create_event_tool
+
+    captured = {}
+
+    async def fake_create_event(**kwargs):
+        captured.update(kwargs)
+        event = AsyncMock()
+        event.to_dict.return_value = {"id": 1, **{k: v for k, v in kwargs.items() if not callable(v)}}
+        return event
+
+    with patch(
+        "fabledassistant.services.tools.calendar.get_user_tz",
+        AsyncMock(return_value=__import__("zoneinfo").ZoneInfo("America/New_York")),
+    ), patch(
+        "fabledassistant.services.tools.calendar.events_create_event",
+        side_effect=fake_create_event,
+    ):
+        result = await create_event_tool(
+            user_id=1,
+            arguments={"title": "Birthday", "start": "2026-09-30"},
+        )
+
+    assert result["success"] is True
+    start_dt = captured["start_dt"]
+    assert start_dt.tzinfo is not None
+    # Converted to UTC: 00:00 NY (EDT, UTC-4) == 04:00 UTC on the same day
+    utc = start_dt.astimezone(timezone.utc)
+    assert (utc.year, utc.month, utc.day) == (2026, 9, 30)
+    assert utc.hour == 4  # EDT offset; would be 5 in EST
+    assert captured["all_day"] is True
+
+
+@pytest.mark.asyncio
+async def test_create_event_naive_datetime_is_user_local():
+    """'2026-03-15T14:30' for a NY user means 2:30pm NY, not 2:30pm UTC."""
+    from fabledassistant.services.tools.calendar import create_event_tool
+
+    captured = {}
+
+    async def fake_create_event(**kwargs):
+        captured.update(kwargs)
+        event = AsyncMock()
+        event.to_dict.return_value = {"id": 1}
+        return event
+
+    with patch(
+        "fabledassistant.services.tools.calendar.get_user_tz",
+        AsyncMock(return_value=__import__("zoneinfo").ZoneInfo("America/New_York")),
+    ), patch(
+        "fabledassistant.services.tools.calendar.events_create_event",
+        side_effect=fake_create_event,
+    ):
+        await create_event_tool(
+            user_id=1,
+            arguments={"title": "Meeting", "start": "2026-03-15T14:30"},
+        )
+
+    utc = captured["start_dt"].astimezone(timezone.utc)
+    # 14:30 NY on 2026-03-15 (after DST start) = 18:30 UTC
+    assert (utc.year, utc.month, utc.day, utc.hour, utc.minute) == (2026, 3, 15, 18, 30)
+
+
+@pytest.mark.asyncio
+async def test_create_event_explicit_offset_is_respected():
+    """Model-supplied timezone offsets must not be overridden by user TZ."""
+    from fabledassistant.services.tools.calendar import create_event_tool
+
+    captured = {}
+
+    async def fake_create_event(**kwargs):
+        captured.update(kwargs)
+        event = AsyncMock()
+        event.to_dict.return_value = {"id": 1}
+        return event
+
+    with patch(
+        "fabledassistant.services.tools.calendar.get_user_tz",
+        AsyncMock(return_value=__import__("zoneinfo").ZoneInfo("America/New_York")),
+    ), patch(
+        "fabledassistant.services.tools.calendar.events_create_event",
+        side_effect=fake_create_event,
+    ):
+        await create_event_tool(
+            user_id=1,
+            arguments={"title": "Meeting", "start": "2026-03-15T14:30+00:00"},
+        )
+
+    utc = captured["start_dt"].astimezone(timezone.utc)
+    assert (utc.hour, utc.minute) == (14, 30)
+
+
+@pytest.mark.asyncio
+async def test_list_events_bare_date_range_covers_local_day():
+    """date_from/date_to as bare dates should cover the full LOCAL day."""
+    from fabledassistant.services.tools.calendar import list_events_tool
+
+    captured = {}
+
+    async def fake_list_events(*, user_id, date_from, date_to):
+        captured["date_from"] = date_from
+        captured["date_to"] = date_to
+        return []
+
+    with patch(
+        "fabledassistant.services.tools.calendar.get_user_tz",
+        AsyncMock(return_value=__import__("zoneinfo").ZoneInfo("America/New_York")),
+    ), patch(
+        "fabledassistant.services.tools.calendar.events_list_events",
+        side_effect=fake_list_events,
+    ):
+        await list_events_tool(
+            user_id=1,
+            arguments={"date_from": "2026-09-30", "date_to": "2026-09-30"},
+        )
+
+    df = captured["date_from"].astimezone(timezone.utc)
+    dt = captured["date_to"].astimezone(timezone.utc)
+    # 2026-09-30 00:00 NY (EDT) = 04:00 UTC same day
+    assert (df.year, df.month, df.day, df.hour) == (2026, 9, 30, 4)
+    # 2026-09-30 23:59:59 NY (EDT) = 03:59:59 UTC next day
+    assert (dt.year, dt.month, dt.day, dt.hour) == (2026, 10, 1, 3)

tests/test_events_service.py

@@ -127,7 +127,7 @@ async def test_update_event_fires_caldav_push():
 @pytest.mark.asyncio
 async def test_tools_calendar_always_available():
     """Calendar tools must appear in get_tools_for_user even without CalDAV."""
-    with patch("fabledassistant.services.tools.is_caldav_configured", new_callable=AsyncMock) as mock_configured:
+    with patch("fabledassistant.services.tools._registry.is_caldav_configured", new_callable=AsyncMock) as mock_configured:
         mock_configured.return_value = False
         from fabledassistant.services.tools import get_tools_for_user
         tools = await get_tools_for_user(user_id=1)

tests/test_research_pipeline.py

@@ -34,8 +34,8 @@ async def test_generate_outline_returns_empty_on_parse_failure():
 
 
 @pytest.mark.asyncio
-async def test_generate_outline_returns_empty_when_fewer_than_3_sections():
-    """_generate_outline returns [] when model returns fewer than 3 valid sections."""
+async def test_generate_outline_returns_empty_when_fewer_than_2_sections():
+    """_generate_outline returns [] when model returns fewer than 2 valid sections."""
     from fabledassistant.services.research import _generate_outline
 
     outline_json = json.dumps([{"title": "Only One", "focus": "Focus"}])
@@ -106,7 +106,7 @@ async def test_synthesize_section_strips_whitespace():
 
 @pytest.mark.asyncio
 async def test_pipeline_creates_section_notes_and_index():
-    """run_research_pipeline creates N section notes + 1 index note, returns index."""
+    """run_research_pipeline creates N section notes + 1 index note with links, returns index."""
     from unittest.mock import MagicMock
 
     outline = [
@@ -117,23 +117,31 @@ async def test_pipeline_creates_section_notes_and_index():
 
     note_id_counter = iter(range(10, 20))
 
-    def _make_note(user_id, title, body, tags, project_id=None):
+    def _make_note(user_id, title, body, tags, project_id=None, parent_id=None):
         n = MagicMock()
         n.id = next(note_id_counter)
         n.title = title
         return n
 
+    mock_update = AsyncMock()
+
     with patch("fabledassistant.services.research._generate_sub_queries", new_callable=AsyncMock, return_value=["q1"]), \
          patch("fabledassistant.services.research._search_searxng", new_callable=AsyncMock, return_value=[{"url": "http://x.com", "title": "X", "snippet": "s"}]), \
          patch("fabledassistant.services.research.fetch_url_content", new_callable=AsyncMock, return_value="content"), \
          patch("fabledassistant.services.research._generate_outline", new_callable=AsyncMock, return_value=outline), \
          patch("fabledassistant.services.research._synthesize_section", new_callable=AsyncMock, side_effect=lambda t, f, s, m: (t, f"Body for {t}")), \
-         patch("fabledassistant.services.research.create_note", new_callable=AsyncMock, side_effect=_make_note):
+         patch("fabledassistant.services.research._generate_executive_summary", new_callable=AsyncMock, return_value="Executive summary text."), \
+         patch("fabledassistant.services.research.create_note", new_callable=AsyncMock, side_effect=_make_note), \
+         patch("fabledassistant.services.research.update_note", new_callable=AsyncMock) as mock_update:
 
         from fabledassistant.services.research import run_research_pipeline
         result = await run_research_pipeline("test topic", user_id=1, model="test-model")
 
     assert result.title == "Research: test topic"
+    # Index note body should be updated with section links
+    mock_update.assert_called_once()
+    updated_body = mock_update.call_args.kwargs.get("body", "")
+    assert "/notes/" in updated_body
 
 
 @pytest.mark.asyncio

tests/test_should_think.py

@@ -0,0 +1,127 @@
+"""Tests for the `_should_think` classifier.
+
+`_should_think` decides whether qwen3-class models should engage chain-of-
+thought for a given chat turn. It is the single source of truth: frontend
+callers pass `think_requested=False` by default and defer to this function,
+while explicit `think_requested=True` acts as an override for curated
+analytical entry points.
+
+These tests lock in the content-based behavior so future tweaks don't
+silently regress the short / long / keyword boundaries.
+"""
+
+import pytest
+
+from fabledassistant.services.generation_task import (
+    _LONG_MESSAGE_CHARS,
+    _SHORT_MESSAGE_CHARS,
+    _should_think,
+)
+
+
+class TestExplicitOverride:
+    def test_override_forces_on_for_empty(self):
+        assert _should_think("", think_requested=True) is True
+
+    def test_override_forces_on_for_short_greeting(self):
+        assert _should_think("hi", think_requested=True) is True
+
+    def test_override_forces_on_for_medium_no_keyword(self):
+        text = "just checking in on the status of things for the week"
+        assert _should_think(text, think_requested=True) is True
+
+
+class TestEmptyAndWhitespace:
+    def test_empty_string_off(self):
+        assert _should_think("", think_requested=False) is False
+
+    def test_none_content_off(self):
+        # _should_think defensively handles None content from upstream callers
+        assert _should_think(None, think_requested=False) is False  # type: ignore[arg-type]
+
+    def test_whitespace_only_off(self):
+        assert _should_think("   \n\t  ", think_requested=False) is False
+
+
+class TestShortMessages:
+    def test_short_greeting_off(self):
+        assert _should_think("hi", think_requested=False) is False
+
+    def test_short_thanks_off(self):
+        assert _should_think("thanks!", think_requested=False) is False
+
+    def test_short_acknowledgement_off(self):
+        assert _should_think("ok sounds good", think_requested=False) is False
+
+    def test_just_below_short_threshold_off(self):
+        text = "a" * (_SHORT_MESSAGE_CHARS - 1)
+        assert _should_think(text, think_requested=False) is False
+
+
+class TestLongMessages:
+    def test_at_long_threshold_on(self):
+        text = "a" * _LONG_MESSAGE_CHARS
+        assert _should_think(text, think_requested=False) is True
+
+    def test_well_above_long_threshold_on(self):
+        text = "x" * (_LONG_MESSAGE_CHARS * 3)
+        assert _should_think(text, think_requested=False) is True
+
+
+class TestMediumMessages:
+    def test_medium_no_keyword_off(self):
+        # Between the short and long thresholds with no reasoning cue.
+        text = "a" * ((_SHORT_MESSAGE_CHARS + _LONG_MESSAGE_CHARS) // 2)
+        assert _should_think(text, think_requested=False) is False
+
+
+class TestKeywordTriggers:
+    @pytest.mark.parametrize(
+        "text",
+        [
+            "why is this failing",
+            "how does caching work here",
+            "how do i configure this",
+            "explain the retry logic",
+            "analyze the latency breakdown",
+            "compare gemma3 vs qwen3 for tool use",
+            "please design the schema for X",
+            "debug this error",
+            "troubleshoot the connection issue",
+            "root cause the outage",
+            "review this PR",
+            "critique my approach",
+            "walk me through the flow",
+            "step by step instructions please",
+            "pros and cons of each option",
+            "help me figure out what's wrong",
+            "discuss this article",  # covers briefing /discuss entry points
+        ],
+    )
+    def test_keyword_forces_on(self, text):
+        assert _should_think(text, think_requested=False) is True
+
+    def test_keyword_case_insensitive(self):
+        assert _should_think("WHY does this break?", think_requested=False) is True
+
+    def test_keyword_in_longer_sentence(self):
+        text = "hey quick one — can you explain what caching does for qwen3"
+        assert _should_think(text, think_requested=False) is True
+
+
+class TestNonTriggers:
+    """Messages that look chatty and should NOT trigger thinking."""
+
+    @pytest.mark.parametrize(
+        "text",
+        [
+            "hey",
+            "yep",
+            "no worries",
+            "got it, thanks",
+            "good morning",
+            "remind me later",  # no reasoning keyword, short
+        ],
+    )
+    def test_chatty_messages_off(self, text):
+        assert _should_think(text, think_requested=False) is False

tests/test_tz_helpers.py

@@ -0,0 +1,63 @@
+"""Tests for user-timezone helpers (services/tz.py)."""
+
+from datetime import datetime
+from unittest.mock import AsyncMock, patch
+from zoneinfo import ZoneInfo
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_get_user_tz_returns_configured_zone():
+    from fabledassistant.services import tz as tz_mod
+
+    with patch.object(tz_mod, "get_setting", AsyncMock(return_value="America/New_York")):
+        zone = await tz_mod.get_user_tz(1)
+    assert zone == ZoneInfo("America/New_York")
+
+
+@pytest.mark.asyncio
+async def test_get_user_tz_falls_back_to_utc_on_bad_input():
+    from fabledassistant.services import tz as tz_mod
+
+    with patch.object(tz_mod, "get_setting", AsyncMock(return_value="Not/AZone")):
+        zone = await tz_mod.get_user_tz(1)
+    assert zone == ZoneInfo("UTC")
+
+
+@pytest.mark.asyncio
+async def test_user_briefing_date_before_4am_returns_yesterday():
+    """00:00–03:59 local still shows yesterday's briefing day."""
+    from fabledassistant.services import tz as tz_mod
+
+    ny = ZoneInfo("America/New_York")
+    # 02:00 NY on 2026-04-13 → briefing day = 2026-04-12
+    fake_now = datetime(2026, 4, 13, 2, 0, tzinfo=ny)
+
+    class _FakeDatetime(datetime):
+        @classmethod
+        def now(cls, tz=None):
+            return fake_now
+
+    with patch.object(tz_mod, "get_user_tz", AsyncMock(return_value=ny)), \
+         patch.object(tz_mod, "datetime", _FakeDatetime):
+        day = await tz_mod.user_briefing_date(1)
+    assert day.isoformat() == "2026-04-12"
+
+
+@pytest.mark.asyncio
+async def test_user_briefing_date_after_4am_returns_today():
+    from fabledassistant.services import tz as tz_mod
+
+    ny = ZoneInfo("America/New_York")
+    fake_now = datetime(2026, 4, 13, 4, 30, tzinfo=ny)
+
+    class _FakeDatetime(datetime):
+        @classmethod
+        def now(cls, tz=None):
+            return fake_now
+
+    with patch.object(tz_mod, "get_user_tz", AsyncMock(return_value=ny)), \
+         patch.object(tz_mod, "datetime", _FakeDatetime):
+        day = await tz_mod.user_briefing_date(1)
+    assert day.isoformat() == "2026-04-13"