
Fastmcp Server
Build, extend, or review MCP servers with Python FastMCP, using documentation matched...
Install
npx skills add PrefectHQ/fastmcp --skill fastmcp-server -g -a claude-code -y- Repository
- PrefectHQ/fastmcp28.3k
- Category
- AI and agents
- Updated
- 3d ago
- License
- Apache-2.0
- Context cost
- Lightabout 1,379 tokens
- Runs scripts
- No, instructions only
What it does
Build, extend, or review MCP servers with Python FastMCP, using documentation matched to the project's version.
Authoring MCP servers with FastMCP
Find the current API
Check the project's installed FastMCP version and dependency constraints.
Fetch the documentation index and read the
relevant pages for that version. The index includes versioned documentation;
select /v3/ pages for FastMCP 3 and unversioned pages for FastMCP 4. Read
individual pages through their .md URLs instead of loading llms-full.txt.
For APIs newer than the installed release, check that release's source or
signatures. Preserve the project's dependency constraints unless an upgrade is
part of the request. For a new project, check the current stable release before
choosing a dependency.
Use the standalone fastmcp package. Its API differs from
mcp.server.fastmcp in the Python MCP SDK.
Author the server
Choose the MCP interface to suit what the server exposes: tools for callable
operations, resources for addressable content, and prompts for reusable message
templates. FastMCP derives tool schemas from Python annotations and descriptions;
use Literal or enums for finite choices and Field descriptions for parameter
semantics. SDK wrappers need to preserve signatures and docstrings, for example
with functools.wraps, or discovery loses that information. Inspect the schema
the client receives, especially after transforms or custom discovery rendering:
requiredness, enum values, and nested fields can disappear from a summary.
Let the intended client tasks guide tool boundaries, descriptions, and result shape. Give clients enough context to use results and recover from errors. The right granularity depends on the service; generated API tools, custom tools, and compositions can all be appropriate.

