💡 摘要
FastMCP通过干净的Python代码简化了使用模型上下文协议构建AI应用程序的过程。
🎯 适合人群
🤖 AI 吐槽: “FastMCP:因为谁不喜欢在AI中添加一点复杂性呢?”
自述文件暗示了潜在的风险,例如依赖供应链问题和网络访问。为了减轻风险,确保定期更新和扫描依赖项以发现漏洞。
FastMCP 🚀
Move fast and make things.
Made with 💙 by Prefect
The Model Context Protocol (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:
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==3.0.0b1For production systems requiring stability, pin to v2:
pip install 'fastmcp<3'
We recommend installing FastMCP with uv:
uv pip install fastmcp
For full installation instructions, including verification and upgrading, see the Installation Guide.
📚 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.
优点
- 与AI工具的集成简化
- 专注于业务逻辑而无需协议复杂性
- 活跃的社区支持和文档
缺点
- 测试版可能存在稳定性问题
- 新用户的学习曲线
- 依赖外部库
相关技能
免责声明:本内容来源于 GitHub 开源项目,仅供展示和评分分析使用。
版权归原作者所有 jlowin.
