Python implementation of the Model Context Protocol (MCP)
- MCP Python SDK
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.
If you haven't created a uv-managed project yet, create one:
uv init mcp-server-demo
cd mcp-server-demoThen add MCP to your project dependencies:
uv add "mcp[cli]"Alternatively, for projects using pip for dependencies:
pip install "mcp[cli]"To run the mcp command with uv:
uv run mcpLet'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.pyAlternatively, you can test it with the MCP Inspector:
mcp dev server.pyThe 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 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 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:
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 let LLMs take actions through your server. Unlike resources, tools are expected to perform computation and have side effects:
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.textPrompts are reusable templates that help LLMs interact with your server effectively:
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?"),
]FastMCP provides an Image class that automatically handles image data:
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")The Context object gives your tools and resources access to MCP capabilities:
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"The fastest way to test and debug your server is with the MCP Inspector:
mcp dev server.py
# Add dependencies
mcp dev server.py --with pandas --with numpy
# Mount local code
mcp dev server.py --with-editable .Once your server is ready, install it in Claude Desktop:
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 .envFor advanced scenarios like custom deployments:
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("My App")
if __name__ == "__main__":
mcp.run()Run it with:
python server.py
# or
mcp run server.pyYou 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.
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.
A simple server demonstrating resources, tools, and prompts:
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}"A more complex example showing database integration:
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)}"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:
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
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())The SDK provides a high-level client interface for connecting to MCP servers:
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())The MCP protocol defines three core primitives that servers can implement:
| Primitive | Control | Description | Example Use |
|---|---|---|---|
| Prompts | User-controlled | Interactive templates invoked by user choice | Slash commands, menu options |
| Resources | Application-controlled | Contextual data managed by the client application | File contents, API responses |
| Tools | Model-controlled | Functions exposed to the LLM to take actions | API calls, data updates |
MCP servers declare capabilities during initialization:
| Capability | Feature Flag | Description |
|---|---|---|
prompts |
listChanged |
Prompt template management |
resources |
subscribelistChanged |
Resource exposure and updates |
tools |
listChanged |
Tool discovery and execution |
logging |
- | Server logging configuration |
completion |
- | Argument completion suggestions |
- Model Context Protocol documentation
- Model Context Protocol specification
- Officially supported servers
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.
This project is licensed under the MIT License - see the LICENSE file for details.