MCP Python SDK

Python implementation of the Model Context Protocol (MCP)

PyPI MIT licensed Python Version Documentation Protocol Specification

This documents v1.x, the maintenance line of the MCP Python SDK. v2 is the current stable release: pip install mcp now installs 2.x. See the v2 documentation and the migration guide for what changed and how to upgrade.

Staying on v1.x? Keep a <2 upper bound on your requirement (for example mcp>=1.28,<2) so an unpinned resolve stays on the 1.x line. v1.x remains supported for existing deployments and continues to receive critical bug fixes and security patches; its documentation is at https://py.sdk.modelcontextprotocol.io/v1/.

Table of Contents

Overview

The Model Context Protocol allows applications to provide context for LLMs in a standardized way, separating the concerns of providing context from the actual LLM interaction. This Python SDK implements the full MCP specification, making it easy to:

  • Build MCP clients that can connect to any MCP server
  • Create MCP servers that expose resources, prompts and tools
  • Use standard transports like stdio, SSE, and Streamable HTTP
  • Handle all MCP protocol messages and lifecycle events

Installation

Adding MCP to your python project

We recommend using uv to manage your Python projects.

If you haven't created a uv-managed project yet, create one:

uv init mcp-server-demo
cd mcp-server-demo

Then add MCP to your project dependencies:

uv add "mcp[cli]<2"

Alternatively, for projects using pip for dependencies:

pip install "mcp[cli]<2"

Running the standalone MCP development tools

To run the mcp command with uv:

uv run mcp

Quickstart

Let's create a simple MCP server that exposes a calculator tool and some data:

"""
FastMCP quickstart example.

Run from the repository root:
    uv run examples/snippets/servers/fastmcp_quickstart.py
"""

from mcp.server.fastmcp import FastMCP

# Create an MCP server
mcp = FastMCP("Demo", json_response=True)


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


# Add a dynamic greeting resource
@mcp.resource("greeting://{name}")
def get_greeting(name: str) -> str:
    """Get a personalized greeting"""
    return f"Hello, {name}!"


# Add a prompt
@mcp.prompt()
def greet_user(name: str, style: str = "friendly") -> str:
    """Generate a greeting prompt"""
    styles = {
        "friendly": "Please write a warm, friendly greeting",
        "formal": "Please write a formal, professional greeting",
        "casual": "Please write a casual, relaxed greeting",
    }

    return f"{styles.get(style, styles['friendly'])} for someone named {name}."


# Run with streamable HTTP transport
if __name__ == "__main__":
    mcp.run(transport="streamable-http")

Full example: examples/snippets/servers/fastmcp_quickstart.py

You can install this server in Claude Code and interact with it right away. First, run the server:

uv run --with "mcp<2" examples/snippets/servers/fastmcp_quickstart.py

Then add it to Claude Code:

claude mcp add --transport http my-server http://localhost:8000/mcp

Alternatively, you can test it with the MCP Inspector. Start the server as above, then in a separate terminal:

npx -y @modelcontextprotocol/inspector

In the inspector UI, connect to http://localhost:8000/mcp.

What is MCP?

The Model Context Protocol (MCP) 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 specifically designed for LLM interactions. MCP servers can:

  • Expose data through Resources (think of these sort of like GET endpoints; they are used to load information into the LLM's context)
  • Provide functionality through Tools (sort of like POST endpoints; they are used to execute code or otherwise produce a side effect)
  • Define interaction patterns through Prompts (reusable templates for LLM interactions)
  • And more!

Documentation

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.

Project links

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Key dates

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  • Released:
Newer release available (2.1.0)

4 maintainers

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Credits

Author: Anthropic, PBC.

Maintainer: David Soria Parra

GitHub Statistics

Data verified by PyPI on Aug 24, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.10

Provides Extra

cli rich ws

Tags

automation git llm mcp

Classifiers

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