Note
FastMCP pioneered Python MCP development, and FastMCP 1.0 was incorporated into the official MCP SDK in 2024.
This is FastMCP 2.0 — the actively maintained, production-ready framework that extends far beyond basic protocol implementation. While the SDK provides core functionality, FastMCP 2.0 delivers everything needed for production: advanced MCP patterns (server composition, proxying, OpenAPI/FastAPI generation, tool transformation), enterprise auth (Google, GitHub, WorkOS, Azure, Auth0, and more), deployment tools, testing utilities, and comprehensive client libraries.
For production MCP applications, install FastMCP: pip install fastmcp
FastMCP is the standard framework for building MCP applications, providing the fastest path from idea to production.
The Model Context Protocol (MCP) is a standardized way to provide context and tools to LLMs. FastMCP makes building production-ready MCP servers simple, with enterprise auth, deployment tools, and a complete ecosystem built in.
# 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's complete documentation is available at gofastmcp.com, including detailed guides, API references, and advanced patterns. This readme provides only a high-level overview.
Documentation is also available in llms.txt format, which is a simple markdown standard that LLMs can consume easily.
There are two ways to access the LLM-friendly documentation:
llms.txtis essentially a sitemap, listing all the pages in the documentation.llms-full.txtcontains the entire documentation. Note this may exceed the context window of your LLM.
- FastMCP v2 🚀
The Model Context Protocol (MCP) lets you build servers that expose data and functionality to LLM applications in a secure, standardized way. It is often described as "the USB-C port for AI", providing a uniform way to connect LLMs to resources they can use. It may be easier to think of it as an 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!
FastMCP provides a high-level, Pythonic interface for building, managing, and interacting with these servers.
FastMCP handles all the complex protocol details so you can focus on building. In most cases, decorating a Python function is all you need — FastMCP handles the rest.
🚀 Fast: High-level interface means less code and faster development
🍀 Simple: Build MCP servers with minimal boilerplate
🐍 Pythonic: Feels natural to Python developers
🔍 Complete: Everything for production — enterprise auth (Google, GitHub, Azure, Auth0, WorkOS), deployment tools, testing frameworks, client libraries, and more
FastMCP provides the shortest path from idea to production. Deploy locally, to the cloud with FastMCP Cloud, or to your own infrastructure.
We recommend installing FastMCP with uv:
uv pip install fastmcpFor full installation instructions, including verification, upgrading from the official MCPSDK, and developer setup, see the Installation Guide.
These are the building blocks for creating MCP servers and clients with FastMCP.
The central object representing your MCP application. It holds your tools, resources, and prompts, manages connections, and can be configured with settings like authentication.
from fastmcp import FastMCP
# Create a server instance
mcp = FastMCP(name="MyAssistantServer")Learn more in the FastMCP Server Documentation.
Tools allow LLMs to perform actions by executing your Python functions (sync or async). Ideal for computations, API calls, or side effects (like POST/PUT). FastMCP handles schema generation from type hints and docstrings. Tools can return various types, including text, JSON-serializable objects, and even images or audio aided by the FastMCP media helper classes.
@mcp.tool
def multiply(a: float, b: float) -> float:
"""Multiplies two numbers."""
return a * bLearn more in the Tools Documentation.
Resources expose read-only data sources (like GET requests). Use @mcp.resource("your://uri"). Use {placeholders} in the URI to create dynamic templates that accept parameters, allowing clients to request specific data subsets.
# Static resource
@mcp.resource("config://version")
def get_version():
return "2.0.1"
# Dynamic resource template
@mcp.resource("users://{user_id}/profile")
def get_profile(user_id: int):
# Fetch profile for user_id...
return {"name": f"User {user_id}", "status": "active"}Learn more in the Resources & Templates Documentation.
Prompts define reusable message templates to guide LLM interactions. Decorate functions with @mcp.prompt. Return strings or Message objects.
@mcp.prompt
def summarize_request(text: str) -> str:
"""Generate a prompt asking for a summary."""
return f"Please summarize the following text:\n\n{text}"Learn more in the Prompts Documentation.
Access MCP session capabilities within your tools, resources, or prompts by adding a ctx: Context parameter. Context provides methods for:
- Logging: Log messages to MCP clients with
ctx.info(),ctx.error(), etc. - LLM Sampling: Use
ctx.sample()to request completions from the client's LLM. - HTTP Request: Use
ctx.http_request()to make HTTP requests to other servers. - Resource Access: Use
ctx.read_resource()to access resources on the server - Progress Reporting: Use
ctx.report_progress()to report progress to the client. - and more...
To access the context, add a parameter annotated as Context to any mcp-decorated function. FastMCP will automatically inject the correct context object when the function is called.
from fastmcp import FastMCP, Context
mcp = FastMCP("My MCP Server")
@mcp.tool
async def process_data(uri: str, ctx: Context):
# Log a message to the client
await ctx.info(f"Processing {uri}...")
# Read a resource from the server
data = await ctx.read_resource(uri)
# Ask client LLM to summarize the data
summary = await ctx.sample(f"Summarize: {data.content[:500]}")
# Return the summary
return summary.textLearn more in the Context Documentation.
Interact with any MCP server programmatically using the fastmcp.Client. It supports various transports (Stdio, SSE, In-Memory) and often auto-detects the correct one. The client can also handle advanced patterns like server-initiated LLM sampling requests if you provide an appropriate handler.
Critically, the client allows for efficient in-memory testing of your servers by connecting directly to a FastMCP server instance via the FastMCPTransport, eliminating the need for process management or network calls during tests.
from fastmcp import Client
async def main():
# Connect via stdio to a local script
async with Client("my_server.py") as client:
tools = await client.list_tools()
print(f"Available tools: {tools}")
result = await client.call_tool("add", {"a": 5, "b": 3})
print(f"Result: {result.content[0].text}")
# Connect via SSE
async with Client("http://localhost:8000/sse") as client:
# ... use the client
passTo use clients to test servers, use the following pattern:
from fastmcp import FastMCP, Client
mcp = FastMCP("My MCP Server")
async def main():
# Connect via in-memory transport
async with Client(mcp) as client:
# ... use the clientFastMCP also supports connecting to multiple servers through a single unified client using the standard MCP configuration format:
from fastmcp import Client
# Standard MCP configuration with multiple servers
config = {
"mcpServers": {
"weather": {"url": "https://weather-api.example.com/mcp"},
"assistant": {"command": "python", "args": ["./assistant_server.py"]}
}
}
# Create a client that connects to all servers
client = Client(config)
async def main():
async with client:
# Access tools and resources with server prefixes
forecast = await client.call_tool("weather_get_forecast", {"city": "London"})
answer = await client.call_tool("assistant_answer_question", {"query": "What is MCP?"})Learn more in the Client Documentation and Transports Documentation.
FastMCP provides comprehensive authentication support that sets it apart from basic MCP implementations. Secure your servers and authenticate your clients with the same enterprise-grade providers used by major corporations.
Built-in OAuth Providers:
- GitHub
- Microsoft Azure
- Auth0
- WorkOS
- Descope
- JWT/Custom
- API Keys
Protecting a server takes just two lines:
from fastmcp.server.auth import GoogleProvider
auth = GoogleProvider(client_id="...", client_secret="...", base_url="https://myserver.com")
mcp = FastMCP("Protected Server", auth=auth)Connecting to protected servers is even simpler:
async with Client("https://protected-server.com/mcp", auth="oauth") as client:
# Automatic browser-based OAuth flow
result = await client.call_tool("protected_tool")Why FastMCP Auth Matters:
- Production-Ready: Persistent storage, token refresh, comprehensive error handling
- Zero-Config OAuth: Just pass
auth="oauth"for automatic setup - Enterprise Integration: WorkOS SSO, Azure Active Directory, Auth0 tenants
- Developer Experience: Automatic browser launch, local callback server, environment variable support
- Advanced Architecture: Full OIDC support, Dynamic Client Registration (DCR), and unique OAuth proxy pattern that enables DCR with any provider
Authentication this comprehensive is unique to FastMCP 2.0.
Learn more in the Authentication Documentation for servers and clients.
FastMCP supports every deployment scenario from local development to global scale:
Development: Run locally with a single command
fastmcp run server.pyProduction: Deploy to FastMCP Cloud — Remote MCP that just works
- Instant HTTPS endpoints
- Built-in authentication
- Zero configuration
- Free for personal servers
Self-Hosted: Use HTTP or SSE transports for your own infrastructure
mcp.run(transport="http", host="0.0.0.0", port=8000)Learn more in the Deployment Documentation.
FastMCP introduces powerful ways to structure and compose your MCP applications.
Create a FastMCP server that acts as an intermediary for another local or remote MCP server using FastMCP.as_proxy(). This is especially useful for bridging transports (e.g., remote SSE to local Stdio) or adding a layer of logic to a server you don't control.
Learn more in the Proxying Documentation.
Build modular applications by mounting multiple FastMCP instances onto a parent server using mcp.mount() (live link) or mcp.import_server() (static copy).
Learn more in the Composition Documentation.
Automatically generate FastMCP servers from existing OpenAPI specifications (FastMCP.from_openapi()) or FastAPI applications (FastMCP.from_fastapi()), instantly bringing your web APIs to the MCP ecosystem.
Learn more: OpenAPI Integration | FastAPI Integration.
The main way to run a FastMCP server is by calling the run() method on your server instance:
# server.py
from fastmcp import FastMCP
mcp = FastMCP("Demo 🚀")
@mcp.tool
def hello(name: str) -> str:
return f"Hello, {name}!"
if __name__ == "__main__":
mcp.run() # Default: uses STDIO transportFastMCP supports three transport protocols:
STDIO (Default): Best for local tools and command-line scripts.
mcp.run(transport="stdio") # Default, so transport argument is optionalStreamable HTTP: Recommended for web deployments.
mcp.run(transport="http", host="127.0.0.1", port=8000, path="/mcp")SSE: For compatibility with existing SSE clients.
mcp.run(transport="sse", host="127.0.0.1", port=8000)See the Running Server Documentation for more details.
Contributions are the core of open source! We welcome improvements and features.
- Python 3.10+
- uv (Recommended for environment management)
-
Clone the repository:
git clone https://github.com/jlowin/fastmcp.git cd fastmcp -
Create and sync the environment:
uv sync
This installs all dependencies, including dev tools.
-
Activate the virtual environment (e.g.,
source .venv/bin/activateor via your IDE).
FastMCP has a comprehensive unit test suite. All PRs must introduce or update tests as appropriate and pass the full suite.
Run tests using pytest:
pytestor if you want an overview of the code coverage
uv run pytest --cov=src --cov=examples --cov-report=htmlFastMCP uses pre-commit for code formatting, linting, and type-checking. All PRs must pass these checks (they run automatically in CI).
Install the hooks locally:
uv run pre-commit installThe hooks will now run automatically on git commit. You can also run them manually at any time:
pre-commit run --all-files
# or via uv
uv run pre-commit run --all-files- Fork the repository on GitHub.
- Create a feature branch from
main. - Make your changes, including tests and documentation updates.
- Ensure tests and pre-commit hooks pass.
- Commit your changes and push to your fork.
- Open a pull request against the
mainbranch ofjlowin/fastmcp.
Please open an issue or discussion for questions or suggestions before starting significant work!
![@marvin-context-protocol[bot]](https://avatars.githubusercontent.com/in/1750662?s=64&v=4){kind=small}
