L'électron rare
20aed903ba
- Voice pipeline: ESP32 WebSocket client → voice bridge → LLM → Piper TTS (Tower :8001) - Hints engine: 3 puzzles (LA_440, LEFOU_PIANO, QR_FINALE), anti-cheat, 3 hint levels - MCP hardware server: 6 tools (puzzle, audio, LED, camera, scenario, status), stdio transport - Analytics: ESP32 module + 6 web endpoints + Dashboard UI with chat interface - Security: auth middleware (Bearer NVS), rate limiting, input validation on 30 endpoints - Frontend: code-split (1.1MB → 210KB initial), ErrorBoundary, API timeout, WS reconnect - Tests: 24 Python + 38 TypeScript + 18 MCP = 80 project tests (+ 19 mascarade) - Specs: AI_INTEGRATION_SPEC, MCP_HARDWARE_SERVER_SPEC, QA_TEST_MATRIX_SPEC - Docs: SECURITY, DEPLOYMENT_RUNBOOK, voice pipeline guide, AI architecture map - 6 AI agent definitions (.github/agents/ai_*.md) - TUI orchestration script (tools/dev/zacus_tui.py) - Docker compose TTS for Tower + KXKM-AI - CHANGELOG, README, mkdocs.yml updated - Cycle detection (DFS) in runtime3 validator - Sprint plan: plans/SPRINT_AI_INTEGRATION.md Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
42 lines
1.7 KiB
Markdown
42 lines
1.7 KiB
Markdown
# Custom Agent – AI MCP Hardware Server
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## Scope
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MCP server bridging LLM tool calls to ESP32 hardware actions (lights, motors, sensors, locks).
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## Technologies
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- MCP protocol (Model Context Protocol), JSON-RPC 2.0
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- mascarade MCP client infrastructure
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- ESP32 web API (HTTP + WebSocket)
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## Do
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- Define MCP tool schemas for each hardware action (e.g., `set_light`, `read_sensor`, `unlock_door`).
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- Implement JSON-RPC 2.0 transport (stdio + HTTP/SSE for remote).
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- Add device discovery via mDNS or static registry.
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- Enforce auth tokens between mascarade and MCP server.
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- Return structured results with status codes and sensor readings.
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## Must Not
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- Expose hardware tools without authentication (token or mutual TLS).
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- Allow unbounded concurrent tool calls to the same device (serialize per-device).
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- Commit auth tokens or secrets to git.
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## Dependencies
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- mascarade MCP infrastructure — client registration, tool routing.
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- ESP32 web API — HTTP endpoints on each device for hardware control.
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## Test Gates
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- Tool call round-trip latency < 500 ms (mascarade → MCP server → ESP32 → response).
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- 100% success rate on all defined tools against a live or mock device.
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## References
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- MCP specification: https://modelcontextprotocol.io
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- mascarade MCP client: `/Users/electron/mascarade/core/mcp/`
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## Plan d'action
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1. Valider le schéma des outils MCP.
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- run: python3 tools/ai/mcp_schema_validate.py --schema mcp/tools.json
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2. Tester la latence aller-retour sur un device mock.
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- run: python3 tools/ai/mcp_latency_bench.py --target mock --max-latency 500
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3. Vérifier l'authentification et la découverte des devices.
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- run: python3 tools/ai/mcp_auth_test.py --require-token
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