Example MCP Server with FastMCP (Python)
Overview
This repository provides an educational example of a Model Context Protocol (MCP) server implemented in Python using the FastMCP library. It demonstrates how to expose tools, resources, and prompts to AI clients, enabling seamless integration with applications like IDEs, chatbots, and agent frameworks.
What is MCP?
Model Context Protocol (MCP) is an open protocol that standardizes how AI applications connect to external tools and data sources. MCP servers expose:
Tools: Executable functions that can be called by AI clients
Resources: Data sources for context (files, APIs, etc.)
Prompts: Reusable templates for interactions
Learn more: modelcontextprotocol.io
Why FastMCP?
FastMCP is a Python library for building MCP servers quickly and easily. It provides:
Simple API for defining tools, resources, and prompts
Support for stdio and HTTP transports
Type-safe schemas for tool inputs/outputs
Integration with popular Python frameworks
How does MCP work?
MCP uses a client-server architecture:
Host: The AI application (e.g., VS Code, Claude Desktop)
Client: Connects to one or more MCP servers
Server: Exposes tools, resources, and prompts
Servers declare their capabilities during initialization. Tools are listed and can be invoked by the client or model. FastMCP makes it easy to implement these features in Python.
Example Features
Define Python functions as MCP tools
Expose resources (e.g., files, API data)
Add prompts for structured interactions
Support for both stdio and HTTP transports
Usage
Install FastMCP:
pip install fastmcpRun the example server:
python mcp_server.pyConnect an MCP-compatible client (e.g., Claude Desktop, VS Code, etc.) to the server.
See mcp_server.py for example code.
References
Note: This example is for educational purposes. Always review server code and tool definitions before connecting to any AI application.
This server cannot be installed
An educational MCP server example built with FastMCP that demonstrates how to expose tools, resources, and prompts to AI clients. Provides a learning foundation for building MCP servers with Python and integrating them with AI applications like IDEs and chatbots.