- Python 100%
19c5c507cc- Client task support is opt-in via importing fastmcp_tasks (drop the core auto-load of companion packages); a plain Client never advertises tasks. - A worker restores the submitting caller's auth token and headers from the task snapshot into the standard ambient context, so get_access_token() / get_http_headers() work in a distributed worker with no new core hooks. - worker_cli validates the loaded extension's resolved backend, not env defaults, so a constructor-configured Redis worker starts. - Thread the per-call read timeout through task polling; bound ToolTask.wait by its deadline; set_elicitation_callback rebuilds internal extensions so a later-set handler answers in-task input. - README imports TaskConfig from fastmcp.utilities.tasks. Co-Authored-By: Claude <noreply@anthropic.com> |
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| fastmcp_remote | ||
| fastmcp_slim | ||
| fastmcp_tasks | ||
| scripts | ||
| skills/fastmcp-client-cli | ||
| tests | ||
| v3-notes | ||
| .ccignore | ||
| .coderabbit.yaml | ||
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| .pre-commit-config.yaml | ||
| .python-version | ||
| AGENTS.md | ||
| CLAUDE.md | ||
| CODE_OF_CONDUCT.md | ||
| CONTRIBUTING.md | ||
| justfile | ||
| LICENSE | ||
| logo.py | ||
| loq.toml | ||
| pyproject.toml | ||
| README.md | ||
| SECURITY.md | ||
| uv.lock | ||
The Model Context Protocol (MCP) connects LLMs to tools and data. FastMCP gives you everything you need to go from prototype to production:
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
Building an effective MCP application is harder than it looks. FastMCP handles all of it. Declare a tool with a Python function, and the schema, validation, and documentation are generated automatically. Connect to a server with a URL, and transport negotiation, authentication, and protocol lifecycle are managed for you. You focus on your logic, and the MCP part just works: with FastMCP, best practices are built in.
That's why FastMCP is the standard framework for working with MCP. FastMCP 1.0 was incorporated into the official MCP Python 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.
FastMCP has three pillars:
Servers Expose tools, resources, and prompts to LLMs. |
Apps Give your tools interactive UIs rendered directly in the conversation. |
Clients Connect to any MCP server — local or remote, programmatic or CLI. |
Servers wrap your Python functions into MCP-compliant tools, resources, and prompts. Clients connect to any server with full protocol support. And Apps give your tools interactive UIs rendered directly in the conversation.
Ready to build? Start with the installation guide or jump straight to the quickstart.
Run FastMCP in production with Horizon
FastMCP is the standard way to build MCP servers. Prefect Horizon is the enterprise MCP gateway for running them safely.
Built by the FastMCP team, Horizon packages the best practices we've learned shipping the world's most popular MCP framework.
Deploy FastMCP servers from GitHub with branch previews and instant rollback. Create a private registry of every MCP your company uses. Secure access with SSO and tool-level RBAC. Get audit logs, observability, and governance across your MCP stack. Remix approved tools into purpose-built endpoints for teams and agents.
Start with FastMCP. Scale with Horizon →
Installation
We recommend installing FastMCP with uv:
uv pip install fastmcp
For full installation instructions, including verification and upgrading, see the Installation Guide.
Upgrading? We have guides for:
Note
If
import fastmcpfails right after apipupgrade from FastMCP 3.2 or earlier, runpip install --force-reinstall fastmcp. See Troubleshooting for why this happens (uvis unaffected).
📚 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.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.
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.