Skip to main content

The fast, Pythonic way to build MCP servers and clients.

Project description

FastMCP Logo

FastMCP 🚀

Move fast and make things.

Made with 💙 by Prefect

Docs Discord PyPI - Version Tests License

jlowin%2Ffastmcp | Trendshift


The Model Context Protocol (MCP) provides a standardized way to connect AI agents to tools and data. FastMCP makes it easy to build MCP applications with clean, Pythonic code:

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()

Why FastMCP

MCP lets you give agents access to your tools and data. But building an effective MCP server is harder than it looks.

Give your agent too much—hundreds of tools, verbose responses—and it gets overwhelmed. Give it too little and it can't do its job. The protocol itself is complex, with layers of serialization, validation, and error handling that have nothing to do with your business logic. And the spec keeps evolving; what worked last month might already need updating.

The real challenge isn't implementing the protocol. It's delivering the right information at the right time.

That's the problem FastMCP solves—and why it's become the standard. FastMCP 1.0 was incorporated into the official MCP SDK in 2024. Today, the actively maintained standalone project is downloaded a million times a day, and some version of FastMCP powers 70% of MCP servers across all languages.

The framework is built on three abstractions that map to the decisions you actually need to make:

  • Components are what you expose: tools, resources, and prompts. Wrap a Python function, and FastMCP handles the schema, validation, and docs.
  • Providers are where components come from: decorated functions, files on disk, OpenAPI specs, remote servers—your logic can live anywhere.
  • Transforms shape what clients see: namespacing, filtering, authorization, versioning. The same server can present differently to different users.

These compose cleanly, so complex patterns don't require complex code. And because FastMCP is opinionated about the details, like serialization, error handling, and protocol compliance, best practices are the path of least resistance. You focus on your logic; the MCP part just works.

Move fast and make things.

Installation

[!Note] FastMCP 3.0 is currently in beta. Install with: pip install fastmcp==3.0.0b1

For production systems requiring stability, pin to v2: pip install 'fastmcp<3'

We recommend installing FastMCP with uv:

uv pip install fastmcp

For full installation instructions, including verification and upgrading, see the Installation Guide.

📚 Documentation

FastMCP's complete documentation is available at gofastmcp.com, including detailed guides, API references, and advanced patterns.

Documentation is also available in llms.txt format, which is a simple markdown standard that LLMs can consume easily:

  • llms.txt is essentially a sitemap, listing all the pages in the documentation.
  • llms-full.txt contains the entire documentation. Note this may exceed the context window of your LLM.

Community: Join our Discord server to connect with other FastMCP developers and share what you're building.

Contributing

We welcome contributions! See the Contributing Guide for setup instructions, testing requirements, and PR guidelines.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

iflow_mcp_jlowin_fastmcp-3.0.0.tar.gz (14.2 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

iflow_mcp_jlowin_fastmcp-3.0.0-py3-none-any.whl (586.2 kB view details)

Uploaded Python 3

File details

Details for the file iflow_mcp_jlowin_fastmcp-3.0.0.tar.gz.

File metadata

  • Download URL: iflow_mcp_jlowin_fastmcp-3.0.0.tar.gz
  • Upload date:
  • Size: 14.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.10.0 {"installer":{"name":"uv","version":"0.10.0","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Debian GNU/Linux","version":"13","id":"trixie","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}

File hashes

Hashes for iflow_mcp_jlowin_fastmcp-3.0.0.tar.gz
Algorithm Hash digest
SHA256 2502c3a314f02b5f7a262f1a8eacbb60f3d1ab8939b9796af30e322cfc697e04
MD5 2c9dd22c6adb909350ab4b1711c594b9
BLAKE2b-256 c082e59ca48c16d7cef51cbc4beadf78190fc1344d65e6d86f8b08976bb98df8

See more details on using hashes here.

File details

Details for the file iflow_mcp_jlowin_fastmcp-3.0.0-py3-none-any.whl.

File metadata

  • Download URL: iflow_mcp_jlowin_fastmcp-3.0.0-py3-none-any.whl
  • Upload date:
  • Size: 586.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.10.0 {"installer":{"name":"uv","version":"0.10.0","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Debian GNU/Linux","version":"13","id":"trixie","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}

File hashes

Hashes for iflow_mcp_jlowin_fastmcp-3.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 e2a4f064e3c77843be7c528bc19003ee2e1523434e6830b28323a4bd53da2ac0
MD5 dcb3c46d13f16193a4723df355ad956d
BLAKE2b-256 3f945478ab2c7a90d8f5d0f18bb046d819f89b6aba9e2973b78e9e2c10caedc5

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page