- Python 100%
32f1118a11* perf: expose minimum_check_interval, reduce task pickup latency The Docket Worker polls for new tasks every minimum_check_interval (previously hardcoded to 250ms in pydocket). Expose this setting so users can tune it, default to 50ms, and override to 10ms in tests. This cuts average task pickup latency from ~125ms to ~5ms per task. * perf: reduce task test overhead and eliminate cross-test contamination - Expose minimum_check_interval setting (default 50ms, 10ms in tests) to reduce Docket Worker task pickup latency - Isolate fakeredis per test via unique memory:// URLs to prevent stale _async_blocking tasks from contaminating subsequent tests - Make client disconnect timeout configurable (default 5s, 1s in tests) - Add --durations=50 to CI for passive performance regression detection - Remove 15s timeout band-aids from task test conftest files - Add explicit @pytest.mark.timeout(10) to cancellation tests - Fix deprecated FastMCP.as_proxy() usage in test_task_proxy.py |
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| .github | ||
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| examples | ||
| scripts | ||
| skills/fastmcp-client-cli | ||
| src/fastmcp | ||
| tests | ||
| v3-notes | ||
| .ccignore | ||
| .coderabbit.yaml | ||
| .gitignore | ||
| .pre-commit-config.yaml | ||
| .python-version | ||
| AGENTS.md | ||
| CLAUDE.md | ||
| CODE_OF_CONDUCT.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. When you're ready to deploy, Prefect Horizon offers free hosting for FastMCP users.
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:
📚 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.