Review code for quality, maintainability, and correctness. Use when reviewing pull requests, evaluating code changes, or providing feedback on implementations. Focuses on API design, patterns, and actionable feedback.
Add this skill
npx mdskills install jlowin/code-reviewComprehensive code review checklist with concrete examples and Sentry-specific practices
Move fast and make things.
Made with 💙 by Prefect
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()
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.
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:
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.txt is essentially a sitemap, listing all the pages in the documentation.llms-full.txt contains 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.
We welcome contributions! See the Contributing Guide for setup instructions, testing requirements, and PR guidelines.
Best experience: Claude Code
/plugin marketplace add jlowin/code-reviewThen /plugin menu → select skill → restart. Use /skill-name:init for first-time setup.
Other platforms
Install via CLI
npx mdskills install jlowin/code-reviewReviewing Code is a free, open-source AI agent skill. Review code for quality, maintainability, and correctness. Use when reviewing pull requests, evaluating code changes, or providing feedback on implementations. Focuses on API design, patterns, and actionable feedback.
Install Reviewing Code with a single command:
npx mdskills install jlowin/code-reviewThis downloads the skill files into your project and your AI agent picks them up automatically.
Reviewing Code works with Claude Code, Claude Desktop, Cursor, Vscode Copilot, Windsurf, Continue Dev, Codex, Gemini Cli, Amp, Roo Code, Goose, Opencode, Trae, Qodo, Command Code. Skills use the open SKILL.md format which is compatible with any AI coding agent that reads markdown instructions.