feat: default to qwen2.5-vl-3b and cap tool schema length constraints

- Default LLM_MODEL to qwen2.5-vl-3b across agent, task worker, tests,
  compose, workflow, and docs
- Cap min/max length-style constraints in tool schemas to 1000 before
  LLM requests; llama.cpp rejects grammar repetitions >= 2000 and the
  memory MCP's update_note body maxLength (200000) broke qwen2.5-vl-3b
  with 'Failed to initialize samplers: failed to parse grammar'

AGENTS.md

@@ -51,7 +51,7 @@ Key variables:
 - `AZURE_SPEECH_REGION` — default `eastus`
 - `AZURE_TTS_VOICE` — default voice (`en-US-Ava:DragonHDOmniLatestNeural`)
 - `LLM_BASE_URL` — OpenAI-compatible endpoint (default `http://192.168.86.2:3005/api`, OpenWebUI)
-- `LLM_MODEL` — default model (default `gemma-4-e4b`; switchable live from the web UI)
+- `LLM_MODEL` — default model (default `qwen2.5-vl-3b`; switchable live from the web UI)
 - `LLM_API_KEY` — OpenWebUI API key (bears `sk-…`)
 
 ## Voice Selection
@@ -129,7 +129,7 @@ curl -s "https://eastus.tts.speech.microsoft.com/cognitiveservices/v1" \
 - **Mic requires HTTPS.** Browsers block getUserMedia outside a secure context. nginx serves the UI on `WEB_PORT` (9178 by default) over HTTPS with a self-signed cert whose SAN includes the detected LAN IP. The cert is persisted in `./certs/` and the LiveKit WS is proxied through nginx at `/livekit/`.
 - **No CDN dependencies.** livekit-client UMD bundle is vendored into `web/`; LAN devices may have no internet access.
 - **Transcripts flow over the data channel.** The agent publishes `{type: "transcript", role, text}` JSON on topic "transcript"; the UI renders them. Voice changes flow the other way as `{type: "set_voice", voice}` on topic "voice-control".
-- **Models come from OpenWebUI.** The LLM endpoint is OpenWebUI's OpenAI-compatible API at `LLM_BASE_URL` (default `http://192.168.86.2:3005/api`); chat goes to `/api/chat/completions` and the model list comes from `GET {LLM_BASE_URL}/models`. Plain `/v1/*` routes are NOT routed on that instance (they serve the SPA). Pipe models (`opencode_go_messages.*`, `go-chat.*`, `go-responses.*`) currently fail upstream with auth errors; the local llama.cpp/vLLM models (e.g. `gemma-4-e4b`, `qwen3.8-27b`) work.
+- **Models come from OpenWebUI.** The LLM endpoint is OpenWebUI's OpenAI-compatible API at `LLM_BASE_URL` (default `http://192.168.86.2:3005/api`); chat goes to `/api/chat/completions` and the model list comes from `GET {LLM_BASE_URL}/models`. Plain `/v1/*` routes are NOT routed on that instance (they serve the SPA). Pipe models (`opencode_go_messages.*`, `go-chat.*`, `go-responses.*`) currently fail upstream with auth errors; the local llama.cpp/vLLM models (e.g. `gemma-4-e4b`, `qwen2.5-vl-3b`, `qwen3.8-27b`) work.
 - **Reasoning models may hide their answer.** Gemma (and similar) sometimes spends tokens on hidden reasoning before producing content. The agent implements this with `max_completion_tokens=1000` and a `HopeLLM` subclass that wraps the LLM stream, falling back to `reasoning_content` if `content` is empty.
 - **Azure TTS uses SSML, not JSON.** The REST endpoint requires `Content-Type: application/ssml+xml`. The LiveKit Azure plugin handles this internally.
 - **Voice changes are live.** The web UI sends a data message to the agent; the agent calls `tts.update_options(voice=...)` without restarting. The agent filters data messages by topic ("voice-control") before processing.