bvandeusen
84640a0dc4
Root cause of the 2026-04-29 dentist-appointment incident: the model
called update_event(query="Appointment") when two events had
"Appointment" in their titles. find_events_by_query returned both,
upcoming-first ordered by start_dt — matches[0] was id=2 (a stale
pre-existing event with garbage end_dt), not id=15 (the one the user
just created via the journal flow). update_event_tool silently took
matches[0] and mutated the wrong event.
Fix: a new resolver helper `_resolve_event_for_action` funnels both
update_event_tool and delete_event_tool through one disambiguation
path. Lookup precedence:
- `event_id` → exact get_event lookup, no query at all
- `query` matching exactly one event → proceed
- `query` matching zero → return success=False, "no event found"
- `query` matching 2+ events → return success=False with a
`candidates` array of {id, title, start_dt, location} so the
model can pick one and call again with `event_id`
The candidates list is capped at 8 to keep the model's context tight.
The error message names the count and the next-step ("pass event_id
or refine the query") so the model can self-correct in one turn.
For delete_event, the disambiguation is even more important — the
silent-matches[0] path would have deleted the wrong event outright
rather than just mutating it. The tool description leans into that:
"Deleting the wrong event is a costly user error; never guess."
Tool surface change: `query` and `event_id` are now both optional;
the tool errors clearly when neither is supplied. The model already
knows id values from prior tool results (returned in `data.id`),
which is the natural feeder for the disambiguation flow.
5 new tests in test_calendar_tool_tz.py cover:
- ambiguous query → success=False with candidate list, no mutation
- event_id supplied → bypasses query lookup entirely
- non-existent event_id → clear "no event found" error
- neither identifier → "query or event_id required" error
- same disambiguation enforced for delete_event_tool
46 calendar/events tests pass; ruff clean.
Closes Fable #161.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Fabled Scribe
A self-hosted second brain and project management application with integrated LLM capabilities. Write, organise, and act on your notes and tasks with the help of a local AI assistant — all running on your own hardware.
Features
Notes and tasks with a Markdown editor, sub-tasks, milestones, and kanban project workspaces. AI chat with streaming responses, RAG over your notes, and tool use (web search, calendar, weather). A daily briefing that digests your tasks, RSS feeds, and weather on a schedule. Knowledge graph, per-user/group sharing, PWA with push notifications, an MCP server for external AI clients, and an Android companion app.
Quick Start
Prerequisites: Docker and Docker Compose. 8 GB+ RAM recommended for LLM inference.
Download docker-compose.quickstart.yml from this repo, then:
# Optional but recommended — set a secret key
export SECRET_KEY=your-random-secret-here
docker compose -f docker-compose.quickstart.yml up -d
Open http://localhost:5000. The first user to register becomes admin. Go to Settings → General to pull an LLM model — qwen3:8b or llama3.1:8b are good starting points.
GPU: Ollama runs CPU-only by default. See the comments in
docker-compose.quickstart.ymlto enable NVIDIA GPU passthrough.
Development: To build from source, see Development.
Documentation
| Doc | Contents |
|---|---|
| Architecture | Stack, design decisions, data models, key services |
| Configuration | Environment variables, Docker Compose, production setup, security |
| Features | Detailed feature breakdown and keyboard shortcuts |
| Development | Dev workflow, CI/CD, migrations, release process |
| API Keys & MCP | API key management and Fable MCP install guide |
| SSO / OAuth | OIDC setup for Authentik, Keycloak, and other providers |
| API Reference | All REST API endpoints |
| Android App | Flutter companion app architecture and feature status |
License
This project is privately maintained.