Content
# MCP In Action
## Introduction
MCP In Action is a practical project designed to help developers quickly master the development and application of Model Context Protocol (MCP). This project showcases how to build applications based on MCP through real-world examples, enabling large language models to interact with external tools and data sources, thereby enhancing the model's capabilities.
## Project Structure
This repository contains the following main projects:
### Project 1: mcp_demo
This project demonstrates the basic application of MCP by setting up a client-server architecture that allows the AI model to access real-time weather information.
#### Core Features
- **MCP Server Development**: Build a server from scratch that complies with MCP specifications.
- **MCP Server Debugging**: Provide debugging methods and best practices.
- **MCP Client Development**: Implement a client that communicates with the server.
- **Sampling Implementation**: Show how to utilize MCP for sampling.
- **Desktop Integration**: Integrate MCP services into desktop applications.
#### Advanced Features of MCP Server
- **Prompt Management**: Optimize and manage model prompts.
- **Resource Handling**: Call and manage external resources.
- **Lifecycle Management**: Control the complete lifecycle of MCP services.
- **LangChain Integration**: Use MCP server within the LangChain framework.
- **Cloud Deployment**: Deploy MCP services to the cloud (Alibaba Cloud/mcp.so).
### Project 2: mcp_rag
This project focuses on combining MCP with Retrieval-Augmented Generation (RAG) technology, demonstrating how to achieve advanced knowledge retrieval and information integration capabilities through MCP.
## MCP Development Guide
### MCP Server Development Process
1. **Define Tool Interfaces**: Clarify the functions and parameters of the tools.
2. **Implement MCP Protocol**: Follow MCP specifications to implement the request-response mechanism.
3. **Configure Runtime Environment**: Set up server listening and processing mechanisms.
4. **Testing and Debugging**: Validate that the server functions correctly.
### MCP Client Development Process
1. **Establish Connection**: Implement communication with the MCP server.
2. **Construct Requests**: Create requests according to MCP specifications.
3. **Handle Responses**: Parse and process the data returned by the MCP server.
4. **Error Handling**: Implement a robust error handling mechanism.
## Project Structure
```
mcp-in-action/
├── mcp_demo/ # Basic MCP example project
│ ├── server.py # MCP server implementation
│ ├── client.py # MCP client example
│ ├── tools/ # Collection of tools
│ │ ├── weather.py # Weather query tool
│ │ └── ...
│ ├── utils/ # Utility functions
│ └── configs/ # Configuration files
├── mcp_rag/ # MCP RAG project
│ ├── server.py # RAG server implementation
│ ├── client.py # RAG client
│ ├── indexer/ # Index builder
│ └── retriever/ # Retrieval implementation
└── README.md # Project documentation
```
## Core Concepts
### Model Context Protocol (MCP)
MCP is a protocol that allows large language models to communicate with external tools and services. It defines a standardized interface between the model and external systems, enabling the model to:
- Call external APIs and services
- Access real-time data
- Perform specific functions and computations
- Return structured responses
### Advantages of MCP
- **Capability Expansion**: Enables models to possess capabilities beyond their training data.
- **Real-time Access**: Access the latest information and data.
- **Professional Tool Integration**: Integrate with existing professional tools and services.
- **Standardization**: Provides a unified interface standard.
- **Security**: Controlled permissions and access management.
## Best Practices
### Server Design
- Implement robust error handling.
- Add detailed logging.
- Design reasonable timeout mechanisms.
- Consider concurrent request handling.
### Client Implementation
- Optimize request batching.
- Implement retry mechanisms.
- Cache results of frequently used requests.
- Handle different types of responses.
### Security Considerations
- Implement authentication and authorization.
- Limit resource usage.
- Monitor for abnormal requests.
- Handle sensitive data appropriately.
## Advanced Application Scenarios
- **Customized Assistants**: Create industry-specific AI assistants by combining proprietary data and tools.
- **Knowledge Base Enhancement**: Achieve dynamic knowledge updates and retrieval through RAG.
- **Multimodal Interaction**: Integrate capabilities such as image processing and audio analysis.
- **Automated Workflows**: Build automated business processes based on MCP.
## Contribution Guidelines
We welcome contributions to the project in the following ways:
1. Fork the repository.
2. Create a feature branch (`git checkout -b feature/amazing-feature`).
3. Commit your changes (`git commit -m 'Add some amazing feature'`).
4. Push to the branch (`git push origin feature/amazing-feature`).
5. Create a Pull Request.
## License
This project is licensed under the [Apache License](LICENSE).
## Contact Information
- Project Maintainer: [FlyAIBox](https://github.com/FlyAIBox)
- Issue Feedback: Please use [GitHub Issues](https://github.com/FlyAIBox/mcp-in-action/issues)
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**Model Context Protocol** - Empowering AI models to interact with the world.
