FastMCP
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@FastMCPcreate a tool that returns the current time"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
FastMCP
A fast, lightweight, and high-performance Model Context Protocol (MCP) server implementation designed for seamless AI tool integration.
🚀 Features
High Performance: Optimized for low latency and minimal overhead.
Easy Integration: Seamlessly connects your custom tools and data sources to LLMs.
Lightweight: Minimal dependencies to keep your environment clean.
Developer Friendly: Simple setup with intuitive API patterns.
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🛠️ Installation
Choose the installation command based on your package manager:
# Using pip
pip install fastmcp
# Using npm
npm install fastmcp💻 Quick Start
Here is a quick example to get your FastMCP server up and running:
from fastmcp import FastMCP
# Initialize the server
server = FastMCP("My FastMCP Server")
# Define a custom tool
@server.tool()
def calculate_metrics(a: int, b: int) -> str:
"""Performs a quick high-performance calculation."""
return f"Result: {a * b}"
if __name__ == "__main__":
server.run()⚙️ Configuration
Configure your server connection in your LLM client settings (e.g., Claude Desktop claude_desktop_config.json):
{
"mcpServers": {
"fastmcp": {
"command": "python",
"args": ["path/to/your/server.py"]
}
}
}📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
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