Today's recommendation: FastMCP - A Python tool that allows LLM to communicate elegantly with local tools
FastMCP——Python AI assistant and local tool interaction artifact, simple and elegant, efficient and practical.
Core content:
1. FastMCP framework overview and basic function introduction
2. FastMCP core features: tools, resources, prompt templates, image processing
3. FastMCP usage scenarios and installation guide
With the development of large language models such as Claude and ChatGPT, how to make AI assistants elegantly call local tools and resources has become an important topic. Today, I would like to introduce a practical open source project, FastMCP, which provides a simple and elegant way to build an MCP (Model Context Protocol) server, so that AI assistants can better interact with our local tools.
? What is FastMCP
FastMCP is a high-level framework based on Python that allows developers to build MCP servers with minimal code. With FastMCP, you can easily:
1. Provide various tool functions for AI assistants 2. Expose local resources to AI 3. Define the interaction template 4. Processing multimedia content such as images
The best part is that it's used in a way that's very intuitive to Python programmers. Take a look at this simple example:
from fastmcp import FastMCP
mcp = FastMCP( "Demo ?" )
@mcp.tool()
def add ( a: int , b: int ) -> int :
"""Add two numbers"""
return a + bIt's that easy! Just run one command to give Claude access to the tool:
fastmcp install demo.py
? Core Features
FastMCP mainly provides the following core functions:
1. Tools
• Similar to an API POST endpoint • Support for performing computations and producing side effects • Can handle complex input and output
2️⃣ Resources
• Similar to an API GET endpoint • Context for loading information into LLM • Support for static and dynamic resources
3️⃣ Prompts
• Define reusable interaction patterns • Supports structured message sequences • Help regulate AI interactions
4️⃣ Image processing
• Built-in image data processing • Automatically handles format conversions • Support tools and resources used in
? Usage scenarios
FastMCP is particularly suitable for the following scenarios:
1. Develop AI assistant toolset
• Provide local function extensions for AI assistants such as Claude • Build domain-specific tool chains
2. Database Interaction • Safely expose database query functionality • Provide schema information for AI reference 3. File processing • Read and process local files • Processing multimedia content such as images 4. API Integration • Tools that wrap existing APIs into AI-enabled tools • Provide a unified access interface
? Installation and use
It is recommended to use uv to install FastMCP:
uv pip install fastmcp
macOS users may need to install uv via Homebrew:
brew install uv
?? Development experience
FastMCP provides three operation modes:
1. Development mode : Use MCP Inspector for interactive testing
fastmcp-dev-server.py
2. Claude desktop integration : recommended method for official use
fastmcp install server.py
3. Direct execution : suitable for advanced usage scenarios
if __name__ == "__main__" :
mcp.run()Why FastMCP is recommended
1. Simple and intuitive : using decorator-style API, in line with Python development habits 2. Complete functions : support tools, resources, prompts and other functions 3. Type safety : good type hint support 4. Development-friendly : Provides complete development tools and debugging interface 5. Active maintenance : The project is under active development
summary
FastMCP is a very practical tool that allows us to provide local capability enhancements to AI assistants in the simplest way. Although the project is still under active development, the core functions are already quite complete and can be used in actual projects.
If you are looking for an elegant way to build AI tools, try FastMCP. Project address: https://github.com/jlowin/fastmcp
What impressed me most about this project is its design concept - "High-level interface means less code and faster development." Indeed, sometimes it is much more difficult to make complex things simple than to make simple things complex.
Well, today's sharing ends here, I hope it helps everyone! If you are interested in this project, you may click a star to support the author~
