fix(llm): correct context sizing, honor think requests, broaden delete

Three related fixes uncovered while benchmarking qwen3:14b against 8b:

- pick_num_ctx was only counting message content, missing the ~15K
  tokens of tool schemas. num_ctx=8192 was being selected while actual
  prompt_tokens hit 14K+, causing silent prompt truncation on every
  tool-using request. Now includes json.dumps(tools) in the estimate.
  KV cache priming in app.py and routes/settings.py also fetches tools
  so the primed num_ctx matches what real chat requests will use.

- _should_think's heuristic classifier was overriding explicit
  think=true requests from the frontend toggle and MCP, gating on
  message length and regex patterns. Now a pass-through — the caller
  is the source of truth. quick_capture hardcodes think=False since
  it's a fast classification path that was relying on the old gating.

- delete_note description only mentioned "note or task", so the model
  refused to call it for entries created by save_person / save_place /
  create_list. Description now explicitly lists all five note_types it
  handles.

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

src/fabledassistant/services/llm.py

@@ -38,12 +38,20 @@ def keep_alive_for(model: str) -> str:
     return Config.OLLAMA_KEEP_ALIVE_MAIN
 
 
-def pick_num_ctx(messages: list[dict]) -> int:
-    """Return the smallest context tier that fits *messages* with 25% headroom.
+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