Model Context Protocol (MCP) Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
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@followed by the MCP server name and your instructions, e.g., "@Model Context Protocol (MCP) Serversend a sample MCP message to test the server"
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.
Model Context Protocol (MCP) Server
A basic implementation of a Model Context Protocol server for educational purposes. This server demonstrates the core concepts of MCP and how to implement them in practice using Python.
Features
WebSocket-based communication using FastAPI and WebSockets
Basic MCP message handling with Pydantic models
Support for model context management
Simple client-server interaction
Async/await support for better performance
Getting Started
Create a virtual environment (recommended):
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activateInstall dependencies:
pip install -r requirements.txtStart the development server:
python -m src.serverRun the example client:
python examples/basic_usage.pyProject Structure
src/
├── server.py # Main server implementation
├── types/ # TypeScript type definitions
│ └── mcp.py # MCP protocol types using Pydantic
└── examples/ # Example implementations
└── basic_usage.py # Basic client exampleMCP Protocol Overview
The Model Context Protocol (MCP) is a communication protocol designed for AI model interactions. This implementation includes:
Message format handling using Pydantic models
Context management
Basic protocol operations
Async/await support for better performance
Key Differences from TypeScript Version
Uses FastAPI instead of Express
Implements async/await for better performance
Uses Pydantic for data validation and serialization
More Pythonic code structure and patterns MIT
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