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/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
 
@@ -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: