- Python 100%
252a29e5e6* Reapply span attributes after creation to survive non-forwarding samplers Tracer.start_span builds the span from sampling_result.attributes, not the attributes kwarg — a custom Sampler that returns SamplingResult(RECORD_AND_SAMPLE) without forwarding attributes silently drops everything FastMCP passed at creation time. Reapply the same attributes immediately after span creation (guarded by is_recording()) so on_start hooks and samplers still see them, while the finished span is guaranteed to carry FastMCP's telemetry regardless of sampler behavior. * Restore only missing span attributes, not a blanket reapply Reapplying all attributes after span creation overwrote values a sampler deliberately set (e.g. a redacted mcp.method.name) and inflated dropped-attribute counts when the SDK's attribute limit was hit. Compare against the span's existing attributes and restore only the keys a non-forwarding sampler actually dropped, via a shared restore_missing_attributes() helper in fastmcp.telemetry. * Gate attribute restore on all-or-nothing, not per-key Restoring only missing keys reinserted attributes the SDK's bounded attribute map had already evicted under a low OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT, evicting a different retained key and inflating dropped_attributes beyond what the sampler actually dropped. Gate on none of our attributes being present (plus dropped_attributes == 0) instead — the regression this exists to fix is a sampler dropping everything, and eviction under a limit always leaves some. Renamed restore_missing_attributes to restore_dropped_attributes to match. * Gate attribute restore on empty span, not per-key presence A sampler that intentionally supplies only its own attributes (e.g. to strip component names or resource URIs for privacy/cardinality control) left none of FastMCP's keys on the span, so the previous all-or-nothing gate treated it identically to a bare non-forwarding sampler and restored everything, defeating the filter. Key off the span having no attributes at all instead — a bare sampler leaves it empty, a filtering sampler doesn't. |
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| .claude | ||
| .cursor/rules | ||
| .github | ||
| docs | ||
| examples | ||
| fastmcp_remote | ||
| fastmcp_slim | ||
| scripts | ||
| skills/fastmcp-client-cli | ||
| tests | ||
| v3-notes | ||
| .ccignore | ||
| .coderabbit.yaml | ||
| .gitignore | ||
| .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.