Product Agent MCP Server
A FastMCP server providing tools to manage and query a product database stored in a JSON format. It enables users to perform operations such as filtering products by category and retrieving product data through a LangGraph agent interface.
README
MCP + LangGraph Product Agent (Test Task)
This repo implements:
- MCP server (FastMCP, stdio) with product tools
- LangGraph agent that connects to the MCP server via stdio subprocess
- FastAPI endpoint to chat with the agent
- Dockerfile + docker-compose
- 3+ tests
Project structure
.
├─ app/
│ ├─ api.py
│ ├─ agent/
│ │ ├─ graph.py
│ │ ├─ mcp_client.py
│ │ ├─ mock_llm.py
│ │ ├─ tools_custom.py
│ │ └─ types.py
│ └─ mcp_server/
│ ├─ products_server.py
│ └─ storage.py
├─ data/products.json
├─ tests/
│ └─ test_api.py
├─ Dockerfile
├─ docker-compose.yml
└─ requirements.txt
Run with Docker Compose (recommended)
docker compose up --build
API будет доступен на:
http://localhost:8000/docs- endpoint:
POST http://localhost:8000/api/v1/agent/query
Example request:
curl -X POST "http://localhost:8000/api/v1/agent/query" \
-H "Content-Type: application/json" \
-d '{"query":"Покажи все продукты в категории Электроника"}'
Run locally
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -r requirements.txt
export PRODUCTS_DB_PATH=./data/products.json # Windows: set PRODUCTS_DB_PATH=...
uvicorn app.api:app --reload
Tests
pytest -q
Notes
- MCP server runs via stdio (
python app/mcp_server/products_server.py) and is spawned by the FastMCPClient(...)inside the agent. - The agent uses a mock LLM (rule-based) that outputs a JSON plan, then executes the plan by calling MCP tools + custom tools.
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