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Implements the Model Context Protocol with streamable HTTP communication for building rich-context AI applications.
About
This project serves as a practical TypeScript implementation of the Model Context Protocol (MCP) from the DeepLearning.AI course. It showcases a robust client-server architecture designed for streamable HTTP communication, enabling AI applications to access context through standardized tools and external data resources. Key functionalities include seamless arXiv paper search, efficient information extraction from research papers, and sophisticated tool selection with precise argument parsing, making it an invaluable educational resource for understanding and leveraging MCP.
Key Features
- arXiv paper search functionality
- Streamable HTTP communication
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- Tool selection and argument extraction
- Paper information extraction
- MCP client-server architecture implementation
Use Cases
- Integrating LLMs with external knowledge bases like arXiv
- Demonstrating the Model Context Protocol (MCP) concepts
- Building AI applications that access external tools and data
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