MCP Python SDK#

Table of Contents#

• MCP Python SDK
  • Overview
  • Installation
    • Adding MCP to your python project
    • Running the standalone MCP development tools
  • 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

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 and SSE
• 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:

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

Then add MCP to your project dependencies:

bash
uv add "mcp[cli]"

Alternatively, for projects using pip for dependencies:

bash
pip install "mcp[cli]"

Running the standalone MCP development tools#

To run the mcp command with uv:

bash
uv run mcp

Quickstart#

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

python
# 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:

bash
mcp install server.py

Alternatively, you can test it with the MCP Inspector:

bash
mcp dev server.py

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!

Core Concepts#

Server#

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

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

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("My App", lifespan=app_lifespan)


# Access type-safe lifespan context in tools
@mcp.tool()
def query_db(ctx: Context) -> str:
    """Tool that uses initialized resources"""
    db = ctx.request_context.lifespan_context.db
    return db.query()

Resources#

Resources are how you expose data to LLMs. They're similar to GET endpoints in a REST API - they provide data but shouldn't perform significant computation or have side effects:

python
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("My App")


@mcp.resource("config://app")
def get_config() -> str:
    """Static configuration data"""
    return "App configuration here"


@mcp.resource("users://{user_id}/profile")
def get_user_profile(user_id: str) -> str:
    """Dynamic user data"""
    return f"Profile data for user {user_id}"

Tools#

Tools let LLMs take actions through your server. Unlike resources, tools are expected to perform computation and have side effects:

python
import httpx
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("My App")


@mcp.tool()
def calculate_bmi(weight_kg: float, height_m: float) -> float:
    """Calculate BMI given weight in kg and height in meters"""
    return weight_kg / (height_m**2)


@mcp.tool()
async def fetch_weather(city: str) -> str:
    """Fetch current weather for a city"""
    async with httpx.AsyncClient() as client:
        response = await client.get(f"https://api.weather.com/{city}")
        return response.text

Prompts#

Prompts are reusable templates that help LLMs interact with your server effectively:

python
from mcp.server.fastmcp import FastMCP
from mcp.server.fastmcp.prompts import base

mcp = FastMCP("My App")


@mcp.prompt()
def review_code(code: str) -> str:
    return f"Please review this code:\n\n{code}"


@mcp.prompt()
def debug_error(error: str) -> list[base.Message]:
    return [
        base.UserMessage("I'm seeing this error:"),
        base.UserMessage(error),
        base.AssistantMessage("I'll help debug that. What have you tried so far?"),
    ]

Images#

FastMCP provides an Image class that automatically handles image data:

python
from mcp.server.fastmcp import FastMCP, Image
from PIL import Image as PILImage

mcp = FastMCP("My App")


@mcp.tool()
def create_thumbnail(image_path: str) -> Image:
    """Create a thumbnail from an image"""
    img = PILImage.open(image_path)
    img.thumbnail((100, 100))
    return Image(data=img.tobytes(), format="png")

Context#

The Context object gives your tools and resources access to MCP capabilities:

python
from mcp.server.fastmcp import FastMCP, Context

mcp = FastMCP("My App")


@mcp.tool()
async def long_task(files: list[str], ctx: Context) -> str:
    """Process multiple files with progress tracking"""
    for i, file in enumerate(files):
        ctx.info(f"Processing {file}")
        await ctx.report_progress(i, len(files))
        data, mime_type = await ctx.read_resource(f"file://{file}")
    return "Processing complete"

Running Your Server#

Development Mode#

The fastest way to test and debug your server is with the MCP Inspector:

bash
mcp dev server.py

# Add dependencies
mcp dev server.py --with pandas --with numpy

# Mount local code
mcp dev server.py --with-editable .

Claude Desktop Integration#

Once your server is ready, install it in Claude Desktop:

bash
mcp install server.py

# Custom name
mcp install server.py --name "My Analytics Server"

# Environment variables
mcp install server.py -v API_KEY=abc123 -v DB_URL=postgres://...
mcp install server.py -f .env

Direct Execution#

For advanced scenarios like custom deployments:

python
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("My App")

if __name__ == "__main__":
    mcp.run()

Run it with:

bash
python server.py
# or
mcp run server.py

Mounting to an Existing ASGI Server#

You can mount the SSE server to an existing ASGI server using the sse_app method. This allows you to integrate the SSE server with other ASGI applications.

python
from starlette.applications import Starlette
from starlette.routing import Mount, Host
from mcp.server.fastmcp import FastMCP


mcp = FastMCP("My App")

# Mount the SSE server to the existing ASGI server
app = Starlette(
    routes=[
        Mount('/', app=mcp.sse_app()),
    ]
)

# or dynamically mount as host
app.router.routes.append(Host('mcp.acme.corp', app=mcp.sse_app()))

For more information on mounting applications in Starlette, see the Starlette documentation.

Examples#

Echo Server#

A simple server demonstrating resources, tools, and prompts:

python
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("Echo")


@mcp.resource("echo://{message}")
def echo_resource(message: str) -> str:
    """Echo a message as a resource"""
    return f"Resource echo: {message}"


@mcp.tool()
def echo_tool(message: str) -> str:
    """Echo a message as a tool"""
    return f"Tool echo: {message}"


@mcp.prompt()
def echo_prompt(message: str) -> str:
    """Create an echo prompt"""
    return f"Please process this message: {message}"

SQLite Explorer#

A more complex example showing database integration:

python
import sqlite3

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("SQLite Explorer")


@mcp.resource("schema://main")
def get_schema() -> str:
    """Provide the database schema as a resource"""
    conn = sqlite3.connect("database.db")
    schema = conn.execute("SELECT sql FROM sqlite_master WHERE type='table'").fetchall()
    return "\n".join(sql[0] for sql in schema if sql[0])


@mcp.tool()
def query_data(sql: str) -> str:
    """Execute SQL queries safely"""
    conn = sqlite3.connect("database.db")
    try:
        result = conn.execute(sql).fetchall()
        return "\n".join(str(row) for row in result)
    except Exception as e:
        return f"Error: {str(e)}"

Advanced Usage#

Low-Level Server#

For more control, you can use the low-level server implementation directly. This gives you full access to the protocol and allows you to customize every aspect of your server, including lifecycle management through the lifespan API:

python
from contextlib import asynccontextmanager
from collections.abc import AsyncIterator

from fake_database import Database  # Replace with your actual DB type

from mcp.server import Server


@asynccontextmanager
async def server_lifespan(server: Server) -> AsyncIterator[dict]:
    """Manage server startup and shutdown lifecycle."""
    # Initialize resources on startup
    db = await Database.connect()
    try:
        yield {"db": db}
    finally:
        # Clean up on shutdown
        await db.disconnect()


# Pass lifespan to server
server = Server("example-server", lifespan=server_lifespan)


# Access lifespan context in handlers
@server.call_tool()
async def query_db(name: str, arguments: dict) -> list:
    ctx = server.request_context
    db = ctx.lifespan_context["db"]
    return await db.query(arguments["query"])

The lifespan API provides:

• A way to initialize resources when the server starts and clean them up when it stops
• Access to initialized resources through the request context in handlers
• Type-safe context passing between lifespan and request handlers

python
import mcp.server.stdio
import mcp.types as types
from mcp.server.lowlevel import NotificationOptions, Server
from mcp.server.models import InitializationOptions

# Create a server instance
server = Server("example-server")


@server.list_prompts()
async def handle_list_prompts() -> list[types.Prompt]:
    return [
        types.Prompt(
            name="example-prompt",
            description="An example prompt template",
            arguments=[
                types.PromptArgument(
                    name="arg1", description="Example argument", required=True
                )
            ],
        )
    ]


@server.get_prompt()
async def handle_get_prompt(
    name: str, arguments: dict[str, str] | None
) -> types.GetPromptResult:
    if name != "example-prompt":
        raise ValueError(f"Unknown prompt: {name}")

    return types.GetPromptResult(
        description="Example prompt",
        messages=[
            types.PromptMessage(
                role="user",
                content=types.TextContent(type="text", text="Example prompt text"),
            )
        ],
    )


async def run():
    async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
        await server.run(
            read_stream,
            write_stream,
            InitializationOptions(
                server_name="example",
                server_version="0.1.0",
                capabilities=server.get_capabilities(
                    notification_options=NotificationOptions(),
                    experimental_capabilities={},
                ),
            ),
        )


if __name__ == "__main__":
    import asyncio

    asyncio.run(run())

Writing MCP Clients#

The SDK provides a high-level client interface for connecting to MCP servers:

python
from mcp import ClientSession, StdioServerParameters, types
from mcp.client.stdio import stdio_client

# Create server parameters for stdio connection
server_params = StdioServerParameters(
    command="python",  # Executable
    args=["example_server.py"],  # Optional command line arguments
    env=None,  # Optional environment variables
)


# Optional: create a sampling callback
async def handle_sampling_message(
    message: types.CreateMessageRequestParams,
) -> types.CreateMessageResult:
    return types.CreateMessageResult(
        role="assistant",
        content=types.TextContent(
            type="text",
            text="Hello, world! from model",
        ),
        model="gpt-3.5-turbo",
        stopReason="endTurn",
    )


async def run():
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(
            read, write, sampling_callback=handle_sampling_message
        ) as session:
            # Initialize the connection
            await session.initialize()

            # List available prompts
            prompts = await session.list_prompts()

            # Get a prompt
            prompt = await session.get_prompt(
                "example-prompt", arguments={"arg1": "value"}
            )

            # List available resources
            resources = await session.list_resources()

            # List available tools
            tools = await session.list_tools()

            # Read a resource
            content, mime_type = await session.read_resource("file://some/path")

            # Call a tool
            result = await session.call_tool("tool-name", arguments={"arg1": "value"})


if __name__ == "__main__":
    import asyncio

    asyncio.run(run())

MCP Primitives#

The MCP protocol defines three core primitives that servers can implement:

Server Capabilities#

MCP servers declare capabilities during initialization:

Documentation#

• Model Context Protocol documentation
• Model Context Protocol specification
• Officially supported servers

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.