The Model Context Protocol (MCP) is a new, standardized way to provide context and tools to your LLMs, and FastMCP makes building MCP servers and clients simple and intuitive. Create tools, expose resources, define prompts, and connect components with clean, Pythonic code.
# server.py
from fastmcp import FastMCP
mcp = FastMCP("Demo 🚀")
@mcp.tool()
def add(a: int, b: int) -> int:
"""Add two numbers"""
return a + b
if __name__ == "__main__":
mcp.run()Run the server locally:
fastmcp run server.pyFastMCP handles the complex protocol details and server management, letting you focus on building great tools and applications. It's designed to feel natural to Python developers.
- What is MCP?
- Why FastMCP?
- Key Features
- What's New in v2?
- Documentation
- Core Concepts
- Advanced Features
- Running Your Server
- Examples
- Contributing
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 GET endpoints; load info into context)
- Provide functionality through Tools (think POST/PUT endpoints; execute actions)
- Define interaction patterns through Prompts (reusable templates)
- And more!
FastMCP provides a high-level, Pythonic interface for building and interacting with these servers.
The MCP protocol is powerful but implementing it involves a lot of boilerplate - server setup, protocol handlers, content types, error management. FastMCP handles all the complex protocol details and server management, so you can focus on building great tools. It’s designed to be high-level and Pythonic; in most cases, decorating a function is all you need.
FastMCP aims to be:
🚀 Fast: High-level interface means less code and faster development
🍀 Simple: Build MCP servers with minimal boilerplate
🐍 Pythonic: Feels natural to Python developers
🔍 Complete: FastMCP aims to provide a full implementation of the core MCP specification for both servers and clients
- Create servers with minimal boilerplate using intuitive decorators
- Proxy existing servers to modify configuration or transport
- Compose servers by into complex applications
- Generate servers from OpenAPI specs or FastAPI objects
- Interact with MCP servers programmatically
- Connect to any MCP server using any transport
- Test your servers without manual intervention
- Innovate with core MCP capabilities like LLM sampling
FastMCP 1.0 made it so easy to build MCP servers that it's now part of the official Model Context Protocol Python SDK! For basic use cases, you can use the upstream version by importing mcp.server.fastmcp.FastMCP (or installing fastmcp=1.0).
Based on how the MCP ecosystem is evolving, FastMCP 2.0 builds on that foundation to introduce a variety of new features (and more experimental ideas). It adds advanced features like proxying and composing MCP servers, as well as automatically generating them from OpenAPI specs or FastAPI objects. FastMCP 2.0 also introduces new client-side functionality like LLM sampling.
📚 FastMCP's documentation is available at gofastmcp.com.
We strongly recommend installing FastMCP with uv, as it is required for deploying servers via the CLI:
uv pip install fastmcpNote: on macOS, uv may need to be installed with Homebrew (brew install uv) in order to make it available to the Claude Desktop app.
For development, install with:
# Clone the repo first
git clone https://github.com/jlowin/fastmcp.git
cd fastmcp
# Install with dev dependencies
uv syncLet's create a simple MCP server that exposes a calculator tool and some data:
# server.py
from 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:
fastmcp install server.py
These are the building blocks for creating MCP servers, using the familiar decorator-based approach.
The central object representing your MCP application. It handles connections, protocol details, and routing.
from fastmcp import FastMCP
# Create a named server
mcp = FastMCP("My App")
# Specify dependencies needed when deployed via `fastmcp install`
mcp = FastMCP("My App", dependencies=["pandas", "numpy"])Tools allow LLMs to perform actions by executing your Python functions. They are ideal for tasks that involve computation, external API calls, or side effects.
Decorate synchronous or asynchronous functions with @mcp.tool(). FastMCP automatically generates the necessary MCP schema based on type hints and docstrings. Pydantic models can be used for complex inputs.
import httpx
from pydantic import BaseModel
class UserInfo(BaseModel):
user_id: int
notify: bool = False
@mcp.tool()
async def send_notification(user: UserInfo, message: str) -> dict:
"""Sends a notification to a user if requested."""
if user.notify:
# Simulate sending notification
print(f"Notifying user {user.user_id}: {message}")
return {"status": "sent", "user_id": user.user_id}
return {"status": "skipped", "user_id": user.user_id}
@mcp.tool()
def get_stock_price(ticker: str) -> float:
"""Gets the current price for a stock ticker."""
# Replace with actual API call
prices = {"AAPL": 180.50, "GOOG": 140.20}
return prices.get(ticker.upper(), 0.0)Resources expose data to LLMs. They should primarily provide information without significant computation or side effects (like GET requests).
Decorate functions with @mcp.resource("your://uri"). Use curly braces {} in the URI to define dynamic resources (templates) where parts of the URI become function parameters.
# Static resource returning simple text
@mcp.resource("config://app-version")
def get_app_version() -> str:
"""Returns the application version."""
return "v2.1.0"
# Dynamic resource template expecting a 'user_id' from the URI
@mcp.resource("db://users/{user_id}/email")
async def get_user_email(user_id: str) -> str:
"""Retrieves the email address for a given user ID."""
# Replace with actual database lookup
emails = {"123": "[email protected]", "456": "[email protected]"}
return emails.get(user_id, "[email protected]")
# Resource returning JSON data
@mcp.resource("data://product-categories")
def get_categories() -> list[str]:
"""Returns a list of available product categories."""
return ["Electronics", "Books", "Home Goods"]Prompts define reusable templates or interaction patterns for the LLM. They help guide the LLM on how to use your server's capabilities effectively.
Decorate functions with @mcp.prompt(). The function should return the desired prompt content, which can be a simple string, a Message object (like UserMessage or AssistantMessage), or a list of these.
from fastmcp.prompts.base import UserMessage, AssistantMessage
@mcp.prompt()
def ask_review(code_snippet: str) -> str:
"""Generates a standard code review request."""
return f"Please review the following code snippet for potential bugs and style issues:\n```python\n{code_snippet}\n```"
@mcp.prompt()
def debug_session_start(error_message: str) -> list[Message]:
"""Initiates a debugging help session."""
return [
UserMessage(f"I encountered an error:\n{error_message}"),
AssistantMessage("Okay, I can help with that. Can you provide the full traceback and tell me what you were trying to do?")
]Gain access to MCP server capabilities within your tool or resource functions by adding a parameter type-hinted with fastmcp.Context.
from fastmcp import Context, FastMCP
mcp = FastMCP("Context Demo")
@mcp.resource("system://status")
async def get_system_status(ctx: Context) -> dict:
"""Checks system status and logs information."""
await ctx.info("Checking system status...")
# Perform checks
await ctx.report_progress(1, 1) # Report completion
return {"status": "OK", "load": 0.5, "client": ctx.client_id}
@mcp.tool()
async def process_large_file(file_uri: str, ctx: Context) -> str:
"""Processes a large file, reporting progress and reading resources."""
await ctx.info(f"Starting processing for {file_uri}")
# Read the resource using the context
file_content_resource = await ctx.read_resource(file_uri)
file_content = file_content_resource[0].content # Assuming single text content
lines = file_content.splitlines()
total_lines = len(lines)
for i, line in enumerate(lines):
# Process line...
if (i + 1) % 100 == 0: # Report progress every 100 lines
await ctx.report_progress(i + 1, total_lines)
await ctx.info(f"Finished processing {file_uri}")
return f"Processed {total_lines} lines."The Context object provides:
- Logging:
ctx.debug(),ctx.info(),ctx.warning(),ctx.error() - Progress Reporting:
ctx.report_progress(current, total) - Resource Access:
await ctx.read_resource(uri) - Request Info:
ctx.request_id,ctx.client_id - Sampling (Advanced):
await ctx.sample(...)to ask the connected LLM client for completions.
Easily handle image input and output using the fastmcp.Image helper class.
from fastmcp import FastMCP, Image
from PIL import Image as PILImage
import io
mcp = FastMCP("Image Demo")
@mcp.tool()
def create_thumbnail(image_data: Image) -> Image:
"""Creates a 100x100 thumbnail from the provided image."""
img = PILImage.open(io.BytesIO(image_data.data)) # Assumes image_data received as Image with bytes
img.thumbnail((100, 100))
buffer = io.BytesIO()
img.save(buffer, format="PNG")
# Return a new Image object with the thumbnail data
return Image(data=buffer.getvalue(), format="png")
@mcp.tool()
def load_image_from_disk(path: str) -> Image:
"""Loads an image from the specified path."""
# Handles reading file and detecting format based on extension
return Image(path=path)FastMCP handles the conversion to/from the base64-encoded format required by the MCP protocol.
The Client class lets you interact with any MCP server (not just FastMCP ones) from Python code:
from fastmcp import Client
async with Client("path/to/server") as client:
# Call a tool
result = await client.call_tool("weather", {"location": "San Francisco"})
print(result)
# Read a resource
res = await client.read_resource("db://users/123/profile")
print(res)You can connect to servers using any supported transport protocol (Stdio, SSE, FastMCP, etc.). If you don't specify a transport, the Client class automatically attempts to detect an appropriate one from your connection string or server object.
The Client class exposes several methods for interacting with MCP servers.
async with Client("path/to/server") as client:
# List available tools
tools = await client.list_tools()
# List available resources
resources = await client.list_resources()
# Call a tool with arguments
result = await client.call_tool("generate_report", {"user_id": 123})
# Read a resource
user_data = await client.read_resource("db://users/123/profile")
# Get a prompt
greeting = await client.get_prompt("welcome", {"name": "Alice"})
# Send progress updates
await client.progress("task-123", 50, 100) # 50% complete
# Basic connectivity testing
await client.ping()These methods correspond directly to MCP protocol operations, making it easy to interact with any MCP-compatible server (not just FastMCP ones).
FastMCP supports various transport protocols for connecting to MCP servers:
from fastmcp import Client
from fastmcp.client.transports import (
SSETransport,
PythonStdioTransport,
FastMCPTransport
)
# Connect to a server over SSE (common for web-based MCP servers)
async with Client(SSETransport("http://localhost:8000/mcp")) as client:
# Use client here...
# Connect to a Python script using stdio (useful for local tools)
async with Client(PythonStdioTransport("path/to/script.py")) as client:
# Use client here...
# Connect directly to a FastMCP server object in the same process
from your_app import mcp_server
async with Client(FastMCPTransport(mcp_server)) as client:
# Use client here...Common transport options include:
SSETransport: Connect to a server via Server-Sent Events (HTTP)PythonStdioTransport: Run a Python script and communicate via stdioFastMCPTransport: Connect directly to a FastMCP server objectWSTransport: Connect via WebSockets
In addition, if you pass a connection string or FastMCP server object to the Client constructor, it will try to automatically detect the appropriate transport.
Sampling is an MCP feature that allows a server to request a completion from the client LLM, enabling sophisticated use cases while maintaining security and privacy on the server.
import marvin # Or any other LLM client
from fastmcp import Client, Context, FastMCP
from fastmcp.client.sampling import RequestContext, SamplingMessage, SamplingParams
# -- SERVER SIDE --
# Create a server that requests LLM completions from the client
mcp = FastMCP("Sampling Example")
@mcp.tool()
async def generate_poem(topic: str, context: Context) -> str:
"""Generate a short poem about the given topic."""
# The server requests a completion from the client LLM
response = await context.sample(
f"Write a short poem about {topic}",
system_prompt="You are a talented poet who writes concise, evocative verses."
)
return response.text
@mcp.tool()
async def summarize_document(document_uri: str, context: Context) -> str:
"""Summarize a document using client-side LLM capabilities."""
# First read the document as a resource
doc_resource = await context.read_resource(document_uri)
doc_content = doc_resource[0].content # Assuming single text content
# Then ask the client LLM to summarize it
response = await context.sample(
f"Summarize the following document:\n\n{doc_content}",
system_prompt="You are an expert summarizer. Create a concise summary."
)
return response.text
# -- CLIENT SIDE --
# Create a client that handles the sampling requests
async def sampling_handler(
messages: list[SamplingMessage],
params: SamplingParams,
ctx: RequestContext,
) -> str:
"""Handle sampling requests from the server using your preferred LLM."""
# Extract the messages and system prompt
prompt = [m.content.text for m in messages if m.content.type == "text"]
system_instruction = params.systemPrompt or "You are a helpful assistant."
# Use your preferred LLM client to generate completions
return await marvin.say_async(
message=prompt,
instructions=system_instruction,
)
# Connect them together
async with Client(mcp, sampling_handler=sampling_handler) as client:
result = await client.call_tool("generate_poem", {"topic": "autumn leaves"})
print(result.content[0].text)This pattern is powerful because:
- The server can delegate text generation to the client LLM
- The server remains focused on business logic and data handling
- The client maintains control over which LLM is used and how requests are handled
- No sensitive data needs to be sent to external APIs
FastMCP exposes the MCP roots functionality, allowing clients to specify which file system roots they can access. This creates a secure boundary for tools that need to work with files. Note that the server must account for client roots explicitly.
from fastmcp import Client, RootsList
# Specify file roots that the client can access
roots = ["file:///path/to/allowed/directory"]
async with Client(mcp_server, roots=roots) as client:
# Now tools in the MCP server can access files in the specified roots
await client.call_tool("process_file", {"filename": "data.csv"})Building on the core concepts, FastMCP v2 introduces powerful features for more complex scenarios:
Create a FastMCP server that acts as an intermediary, proxying requests to another MCP endpoint (which could be a server or another client connection).
Use Cases:
- Transport Conversion: Expose a server running on Stdio (like many local tools) over SSE or WebSockets, making it accessible to web clients or Claude Desktop.
- Adding Functionality: Wrap an existing server to add authentication, request logging, or modified tool behavior.
- Aggregating Servers: Combine multiple backend MCP servers behind a single proxy interface (though
mountmight be simpler for this).
import asyncio
from fastmcp import FastMCP, Client
from fastmcp.client.transports import PythonStdioTransport
# Create a client that connects to the original server
proxy_client = Client(
transport=PythonStdioTransport('path/to/original_stdio_server.py'),
)
# Create a proxy server that connects to the client and exposes its capabilities
proxy = FastMCP.as_proxy(proxy_client, name="Stdio-to-SSE Proxy")
if __name__ == "__main__":
proxy.run(transport='sse')FastMCP.as_proxy is an async classmethod. It connects to the target, discovers its capabilities, and dynamically builds the proxy server instance.
Structure larger MCP applications by creating modular FastMCP servers and "mounting" them onto a parent server. This automatically handles prefixing for tool names and resource URIs, preventing conflicts.
from fastmcp import FastMCP
# --- Weather MCP ---
weather_mcp = FastMCP("Weather Service")
@weather_mcp.tool()
def get_forecast(city: str):
return f"Sunny in {city}"
@weather_mcp.resource("data://temp/{city}")
def get_temp(city: str):
return 25.0
# --- News MCP ---
news_mcp = FastMCP("News Service")
@news_mcp.tool()
def fetch_headlines():
return ["Big news!", "Other news"]
@news_mcp.resource("data://latest_story")
def get_story():
return "A story happened."
# --- Composite MCP ---
mcp = FastMCP("Composite")
# Mount sub-apps with prefixes
mcp.mount("weather", weather_mcp) # Tools prefixed "weather/", resources prefixed "weather+"
mcp.mount("news", news_mcp) # Tools prefixed "news/", resources prefixed "news+"
@mcp.tool()
def ping():
return "Composite OK"
if __name__ == "__main__":
mcp.run()This promotes code organization and reusability for complex MCP systems.
Leverage your existing web APIs by automatically generating FastMCP servers from them.
By default, the following rules are applied:
GETrequests -> MCP resourcesGETrequests with path parameters -> MCP resource templates- All other HTTP methods -> MCP tools
You can override these rules to customize or even ignore certain endpoints.
From FastAPI:
from fastapi import FastAPI
from fastmcp import FastMCP
# Your existing FastAPI application
fastapi_app = FastAPI(title="My Existing API")
@fastapi_app.get("/status")
def get_status():
return {"status": "running"}
@fastapi_app.post("/items")
def create_item(name: str, price: float):
return {"id": 1, "name": name, "price": price}
# Generate an MCP server directly from the FastAPI app
mcp_server = FastMCP.from_fastapi(fastapi_app)
if __name__ == "__main__":
mcp_server.run()From an OpenAPI Specification:
import httpx
import json
from fastmcp import FastMCP
# Load the OpenAPI spec (dict)
# with open("my_api_spec.json", "r") as f:
# openapi_spec = json.load(f)
openapi_spec = { ... } # Your spec dict
# Create an HTTP client to make requests to the actual API endpoint
http_client = httpx.AsyncClient(base_url="https://api.yourservice.com")
# Generate the MCP server
mcp_server = FastMCP.from_openapi(openapi_spec, client=http_client)
if __name__ == "__main__":
mcp_server.run()Choose the method that best suits your needs:
Use fastmcp dev for an interactive testing environment with the MCP Inspector.
fastmcp dev your_server_file.py
# With temporary dependencies
fastmcp dev your_server_file.py --with pandas --with numpy
# With local package in editable mode
fastmcp dev your_server_file.py --with-editable .Use fastmcp install to set up your server for persistent use within the Claude Desktop app. It handles creating an isolated environment using uv.
fastmcp install your_server_file.py
# With a custom name in Claude
fastmcp install your_server_file.py --name "My Analysis Tool"
# With extra packages and environment variables
fastmcp install server.py --with requests -v API_KEY=123 -f .envRun your server script directly for custom deployments or integrations outside of Claude. You manage the environment and dependencies yourself.
Add to your your_server_file.py:
if __name__ == "__main__":
mcp.run() # Assuming 'mcp' is your FastMCP instanceRun with:
python your_server_file.py
# or
uv run python your_server_file.pyIf your FastMCP instance is not named mcp, server, or app, specify it using file:object syntax for the dev and install commands:
fastmcp dev my_module.py:my_mcp_instance
fastmcp install api.py:api_appExplore the examples/ directory for code samples demonstrating various features:
simple_echo.py: Basic tool, resource, and prompt.complex_inputs.py: Using Pydantic models for tool inputs.mount_example.py: Mounting multiple FastMCP servers.sampling.py: Using LLM completions within your MCP server.screenshot.py: Tool returning an Image object.text_me.py: Tool interacting with an external API.memory.py: More complex example with database interaction.
Contributions make the open-source community vibrant! We welcome improvements and features.
- Python 3.10+
- uv
- Clone:
git clone https://github.com/jlowin/fastmcp.git && cd fastmcp - Install Env & Dependencies:
uv venv && uv sync(Activate the.venvafter creation)
Run the test suite:
uv run pytest -vvWe use ruff via pre-commit.
- Install hooks:
pre-commit install - Run checks:
pre-commit run --all-files
- Fork the repository.
- Create a feature branch.
- Make changes, commit, and push to your fork.
- Open a pull request against the
mainbranch ofjlowin/fastmcp.
Please open an issue or discussion for questions or suggestions!
