• Overview
• Installation
• Quickstart
• What is MCP?
• Core Concepts
  • Server
  • Resources
  • Tools
  • Prompts
  • Images
  • Context
• Running Your Server
  • Development Mode
  • Claude Desktop Integration
  • Direct Execution
  • Mounting to an Existing ASGI Server
• Examples
  • Echo Server
  • SQLite Explorer
• Advanced Usage
  • Low-Level Server
  • Writing MCP Clients
  • MCP Primitives
  • Server Capabilities
• Documentation
• Contributing
• License

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 and SSE
• Handle all MCP protocol messages and lifecycle events

We recommend using uv to manage your Python projects. In a uv managed python project, add mcp to dependencies by:

uv add "mcp[cli]"

Alternatively, for projects using pip for dependencies:

pip install mcp

To run the mcp command with uv:

uv run mcp

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

# server.py
from mcp.server.fastmcp import FastMCP

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


# 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}!"

You can install this server in Claude Desktop and interact with it right away by running:

mcp install server.py

Alternatively, you can test it with the MCP Inspector:

mcp dev server.py

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!

The FastMCP server is your core interface to the MCP protocol. It handles connection management, protocol compliance, and message routing:

# Add lifespan support for startup/shutdown with strong typing
from contextlib import asynccontextmanager
from dataclasses import dataclass
from typing import AsyncIterator

from fake_database import Database  # Replace with your actual DB type

from mcp.server.fastmcp import Context, FastMCP

# Create a named server
mcp = FastMCP("My App")

# Specify dependencies for deployment and development
mcp = FastMCP("My App", dependencies=["pandas", "numpy"])


@dataclass
class AppContext:
    db: Database


@asynccontextmanager
async def app_lifespan(server: FastMCP) -> AsyncIterator[AppContext]:
    """Manage application lifecycle with type-safe context"""
    # Initialize on startup
    db = await Database.connect()
    try:
        yield AppContext(db=db)
    finally:
        # Cleanup on shutdown
        await db.disconnect()


# Pass lifespan to server
mcp = FastMCP(