MCP Python SDK

Python implementation of the Model Context Protocol (MCP)

Documentation

The documentation lives at https://py.sdk.modelcontextprotocol.io/.

It has a Get started guide, What's new in v2, the API reference, and the migration guide.

What is MCP?

The Model Context Protocol lets you build servers that expose data and functionality to LLM applications in a secure, standardized way. Think of it like a web API, but designed for LLM interactions. With this SDK you can:

• Build MCP servers that expose tools, resources, and prompts to any MCP host
• Build MCP clients that connect to any MCP server
• Speak every standard transport: stdio, Streamable HTTP, and SSE

Requirements

Python 3.11+.

Installation

uv add "mcp[cli]"      # or: pip install "mcp[cli]"

The cli extra adds the mcp command-line tool (mcp dev, mcp run, mcp install) on top of the SDK; install plain mcp if you don't need it. For one-off commands, uv run --with "mcp[cli]" mcp ... works without a project.

A server in 15 lines

Create a server.py:

from mcp.server import MCPServer

mcp = MCPServer("Demo")


@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two numbers."""
    return a + b


@mcp.resource("greeting://{name}")
def greeting(name: str) -> str:
    """Greet someone by name."""
    return f"Hello, {name}!"

Full example: docs_src/index/tutorial001.py

That's a complete MCP server: one tool, one templated resource. Open it in the MCP Inspector:

uv run mcp dev server.py

Call add with a=1, b=2 and you get 3 back.

Notice what you did not write: no JSON Schema (a: int, b: int is the schema), no request parsing, no validation code, no protocol handling. Two type-hinted Python functions and a docstring.

Get started takes it from here.

A client in 10 lines

The same package is a full MCP client. Serve server.py over HTTP:

uv run mcp run server.py --transport streamable-http

then point a Client at it:

import asyncio

from mcp import Client


async def main() -> None:
    async with Client("http://localhost:8000/mcp") as client:
        result = await client.call_tool("add", {"a": 1, "b": 2})
        print(result.structured_content)  # {'result': 3}


asyncio.run(main())

A URL means Streamable HTTP, the transport you deploy. Client can also launch a local server as a stdio subprocess or take any custom transport; Clients has the rest.

Contributing

We are passionate about supporting contributors of all levels of experience and would love to see you get involved in the project. See the contributing guide to get started.

License

This project is licensed under the MIT License. See the LICENSE file for details.