Demonstrates how to create and deploy a simple server adhering to the Model-Context-Protocol (MCP), providing a basic tool for AI clients.
This project offers a foundational demonstration for building and deploying a server that complies with the Model-Context-Protocol (MCP). MCP facilitates secure communication and local tool execution for large language models and other AI agents. This specific implementation showcases a basic FastMCP server in Python, exposing a simple 'add' tool designed to take two integers and return their sum, making it a perfect starting point for integrating AI models like Claude with local development environments.
Key Features
01Simple FastMCP server implementation
02Basic 'add(x: int, y: int) -> int' mathematical tool
03Packaged as a Python project using pyproject.toml and setuptools
04Ready for installation and deployment
05Command-line entry point for running the server
060 GitHub stars
Use Cases
01Learning and demonstrating Model-Context-Protocol (MCP) server development
02Integrating an MCP server with AI models like Claude for local tool execution
03Developing a foundational tool to extend AI agent capabilities with custom functions
