flowchart TD
    A[User: "What's the weather in London?"] --> B[AI Agent<br/>OpenAI Responses API]
    B --> C[1. Discovers MCP tools]
    B --> D[2. Decides whether to call]
    B --> E[3. Emits function call]
    E --> F[MCP Client<br/>ClientSession + stdio]
    F --> G[initialize]
    F --> H[tools/list]
    F --> I[tools/call]
    I --> J[JSON-RPC 2.0<br/>stdin/stdout]
    J --> K[MCP Server subprocess]
    K --> L[get_current_weather tool]
    K --> M[greeting://{name} resource]

mcp-demo/
├── README.md
├── requirements.txt
├── .env.example
├── pyproject.toml
├── src/
│   ├── mcp_server/
│   │   ├── __init__.py
│   │   ├── server.py      # MCP server entry point
│   │   ├── tools.py       # Tool implementations
│   │   ├── handlers.py    # Request handlers
│   │   └── utils.py       # Shared utilities
│   ├── mcp_client/
│   │   ├── __init__.py
│   │   ├── client.py      # MCP client wrapper
│   │   ├── agent.py       # OpenAI agent integration
│   │   └── runner.py      # Demo runner
│   └── shared/
│       ├── __init__.py
│       └── types.py       # Shared Pydantic models
├── tests/
│   ├── test_server.py
│   └── test_client.py
├── examples/
│   └── demo.ipynb
└── scripts/
    └── run_demo.sh

python -m venv .venv
source .venv/bin/activate        # Linux/macOS
.venv\Scripts\Activate.ps1       # Windows PowerShell

python -m pip install --upgrade pip
pip install -r requirements.txt

cp .env.example .env

OPENAI_API_KEY=your_api_key_here
OPENAI_MODEL=gpt-4.1-mini

python src/mcp_client/runner.py

bash scripts/run_demo.sh

INFO mcp_client.client: -> MCP initialize
INFO mcp_client.client: <- MCP initialize: server=mcp-demo-server
INFO mcp_client.client: -> MCP tools/list
INFO mcp_client.client: <- MCP tools/list: ["get_current_weather"]
INFO mcp_client.agent: User: What's the weather in London?
INFO mcp_client.agent: OpenAI requested tool: get_current_weather {"city":"London","units":"metric"}
INFO mcp_client.client: -> MCP tools/call name=get_current_weather arguments={"city":"London","units":"metric"}
INFO mcp_server.tools: weather lookup city=London units=metric
INFO mcp_client.client: <- MCP tools/call result={"city":"London","temperature":18.0,...}
INFO mcp_client.agent: Final: London is 18°C and partly cloudy.

python src/mcp_server/server.py

pip install "mcp[cli]"
mcp dev src/mcp_server/server.py

get_current_weather(
    city: str,
    units: Literal["metric", "imperial"] = "metric"
) -> WeatherResponse

{
  "city": "London",
  "temperature": 18.0,
  "units": "metric",
  "condition": "partly cloudy",
  "humidity_percent": 72
}

flowchart LR
    A[MCP Tool Schema] --> B[OpenAI Function Tool]
    B --> C[Model Chooses Function]
    C --> D[MCP ClientSession.call_tool]
    D --> E[MCP Server Executes Tool]
    E --> F[Function Call Output]
    F --> G[Final Model Answer]

pytest -q

# Check
ruff check .
ruff format --check .

# Format
ruff format .

// Client -> Server
{"jsonrpc":"2.0","id":1,"method":"initialize","params":{...}}

// Server -> Client
{"jsonrpc":"2.0","id":1,"result":{...}}

// Client -> Server
{"jsonrpc":"2.0","id":2,"method":"tools/list","params":{}}

// Server -> Client
{"jsonrpc":"2.0","id":2,"result":{"tools":[...]}}

// Client -> Server
{"jsonrpc":"2.0","id":3,"method":"tools/call",
 "params":{"name":"get_current_weather","arguments":{"city":"London"}}}

// Server -> Client
{"jsonrpc":"2.0","id":3,"result":{"content":[...],"structuredContent":{...}}}