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Model Context Protocol (mcp) Server Mn0

A Python-based implementation of the Model Context Protocol server for educational purposes, demonstrating the core concepts and practices of MCP, supporting WebSocket communication and asynchronous processing.
2 points
15

What is an MCP server?

The MCP server is a WebSocket-based communication service specifically designed for AI model interaction. It allows clients to establish persistent connections with AI models and manage conversation contexts, enabling the models to understand continuous conversation content.

How to use the MCP server?

Simply install the Python environment, run the server script, and then connect via a WebSocket client to start interacting with the AI model. The server will automatically handle message formats and context management.

Applicable scenarios

Suitable for AI application scenarios that require continuous conversations, such as chatbots, intelligent assistants, and educational tools that need to maintain conversation contexts.

Main features

WebSocket communicationUse the modern WebSocket protocol to achieve real-time two-way communication, which is more efficient than traditional HTTP.
Context managementAutomatically maintain the conversation context, enabling the AI model to understand continuous conversation content.
Asynchronous processingUse Python's async/await to achieve high-performance concurrent processing.
Data validationUse Pydantic to automatically validate the input data format and ensure communication security.

Advantages and limitations

Advantages
Lightweight implementation, easy to understand and modify
Complete type checking and data validation
High-performance asynchronous processing capabilities
Clear protocol definition and documentation
Limitations
Currently only supports Python clients
Lack of production-level security features
The context management function is relatively basic
Limited performance optimization space

How to use

Prepare the environment
Ensure that Python 3.7+ is installed. It is recommended to use a virtual environment.
Install dependencies
Install all Python packages required for the project.
Start the server
Run the main server script.
Connect the client
Use a WebSocket client to connect to ws://localhost:8000/ws.

Usage examples

Simple conversationConduct basic Q&A interactions with the AI model.
Contextual conversationConduct continuous conversations that require context understanding.

Frequently Asked Questions

What version of Python is required?
How to modify the server port?
What types of message formats are supported?
How to extend the protocol functionality?

Related resources

FastAPI documentation
Official documentation for the WebSocket server framework
WebSocket protocol specification
Official standard documentation for the WebSocket protocol
Pydantic documentation
Official documentation for the data validation library
GitHub repository
Project source code and issue tracking
Installation
Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.
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