reviewing-code
🎯Skillfrom jlowin/fastmcp
|I apologize, but I cannot confidently infer the specific functionality of a "reviewing-code" skill from the provided README. The README describes FastMCP as a general framework for connecting AI ag...
Installation
npx skills add https://github.com/jlowin/fastmcp --skill reviewing-codeSkill Details
Overview
# FastMCP 🚀 Move fast and make things. Made with 💙 by [Prefect](https://www.prefect.io/) [](https://gofastmcp.com) [](https://discord.gg/uu8dJCgttd) [](https://pypi.org/project/fastmcp) [](https://github.com/jlowin/fastmcp/actions/workflows/run-tests.yml) [](https://github.com/jlowin/fastmcp/blob/main/LICENSE)
---
The [Model Context Protocol](https://modelcontextprotocol.io) (MCP) provides a standardized way to connect AI agents to tools and data. FastMCP makes it easy to build MCP applications with clean, Pythonic code:
```python
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
MCP lets you give agents access to your tools and data. But building an effective MCP server is harder than it looks.
Give your agent too much—hundreds of tools, verbose responses—and it gets overwhelmed. Give it too little and it can't do its job. The protocol itself is complex, with layers of serialization, validation, and error handling that have nothing to do with your business logic. And the spec keeps evolving; what worked last month might already need updating.
The real challenge isn't implementing the protocol. It's delivering the right information at the right time.
That's the problem FastMCP solves—and why it's become the standard. FastMCP 1.0 was incorporated into the official MCP 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.
The framework is built on three abstractions that map to the decisions you actually need to make:
- Components are what you expose: tools, resources, and prompts. Wrap a Python function, and FastMCP handles the schema, validation, and docs.
- Providers are where components come from: decorated functions, files on disk, OpenAPI specs, remote servers—your logic can live anywhere.
- Transforms shape what clients see: namespacing, filtering, authorization, versioning. The same server can present differently to different users.
These compose cleanly, so complex patterns don't require complex code. And because FastMCP is opinionated about the details, like serialization, error handling, and protocol compliance, best practices are the path of least resistance. You focus on your logic; the MCP part just works.
Move fast and make things.
Installation
> [!Note]
> FastMCP 3.0 is currently in beta. Install with: pip install fastmcp==3.0.0b1
>
> For production systems requiring stability, pin to v2: pip install 'fastmcp<3'
We recommend installing FastMCP with [uv](https://docs.astral.sh/uv/):
```bash
uv pip install fastmcp
```
For full installation instructions, including verification and upgrading, see the [Installation Guide](https://gofastmcp.com/getting-started/installation).
📚 Documentation
FastMCP'