Skills

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.

Read the whole SKILL.md on GitHub

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