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| ### Objective | |
| Use the existing MCP server as the single source of truth for tools/resources/prompts, and have the LangGraph agent call MCP rather than local Python tool implementations. | |
| ### Rationale | |
| - Single source of truth for schemas/behavior (no drift). | |
| - Multi-client support out of the box (Claude Desktop via supergateway, others). | |
| - Clean security boundary via the auth proxy (no local secrets in clients). | |
| ### Architecture | |
| - MCP server: `python -m foodwise.mcp_server` (HTTP transport, token-gated via `auth_proxy` at `/mcp/`). | |
| - Agent: uses Adapter Tools that delegate to MCP over HTTP instead of importing `foodwise.agent.tools`. | |
| - Feature flag: `USE_MCP_TOOLS=1` toggles MCP-backed adapters vs. current local tools during migration. | |
| ### Transport & Auth | |
| - Prefer Authorization header: `Authorization: Bearer $MCP_AUTH_TOKEN`. | |
| - Desktop bridge compatibility: `?key=$MCP_AUTH_TOKEN` supported (bridge only). | |
| - Health check: `GET /health` (no auth) before first tool call. | |
| - Agent should never embed tokens in URLs; use header-based auth. | |
| ### Implementation Plan | |
| 1) Verify server parity | |
| - `fastmcp_tools.py` exposes inventory + shopping tools with typed params and clear descriptions. | |
| - `fastmcp_resources.py` and `fastmcp_prompts.py` registered in `mcp_server/main.py` (already done). | |
| 2) Add MCP client wrapper (Python) | |
| - File: `src/foodwise/mcp_client/client.py` | |
| - Responsibilities: | |
| - Manage base URL, auth header/query param | |
| - Discover tool schemas and call tools over HTTP transport | |
| - Provide minimal typed helpers using Pydantic models (derive from `database/db_models.py` or shared models via `model_dump`) [[Prefer Pydantic model_dump]]. | |
| 3) Agent tool adapters | |
| - File: `src/foodwise/agent/mcp_tools.py` | |
| - Define LangChain/LangGraph `Tool` objects that call the MCP client | |
| - Maintain current tool names/semantics for drop-in replacement | |
| - Switch `agent/agent.py` to use `get_mcp_tools()` when `USE_MCP_TOOLS=1` | |
| 4) Prompts and resources usage | |
| - Keep agent system prompt in `agent/prompts.py` unchanged | |
| - MCP prompts remain for MCP clients; agent doesn’t need them directly | |
| - Optionally fetch MCP resources at the start of an interaction to seed context (later optimization) | |
| 5) Config/env (do not edit pyproject directly) | |
| - `.env`/`env.example` additions: | |
| - Production endpoint (HF Spaces): `MCP_ENDPOINT=https://<your-space>.hf.space/mcp/` | |
| - Local dev alternative: `MCP_ENDPOINT=http://127.0.0.1:7860/mcp/` | |
| - Auth: `MCP_AUTH_TOKEN=...` (or `MCP_AUTH_TOKENS`) | |
| - Toggle: `USE_MCP_TOOLS=1` | |
| - Do NOT include tokens in the URL; clients should send `Authorization: Bearer` headers. | |
| 6) Tests | |
| - Unit tests for MCP client: auth header, timeouts, error propagation, happy-path tool call | |
| - Agent smoke: run `scripts/agent_smoke.py` with `USE_MCP_TOOLS=1` and proxy running; verify same outputs for read paths | |
| ### Rollout | |
| - Phase 1: Behind flag in dev; local tools remain fallback | |
| - Phase 2: Make MCP the default; keep fallback briefly | |
| - Phase 3: Remove local tool implementations once parity confirmed | |
| ### Risks & Mitigations | |
| - Schema drift → Source request/response from shared Pydantic models via `model_dump()` | |
| - Network/auth errors → Early `/health` check, clear error messages, retries where sensible | |
| ### Done Criteria | |
| - With `USE_MCP_TOOLS=1`, agent uses MCP-backed tools for inventory/shopping and passes existing smoke checks |