Documentation Retrieval MCP Server (DOCRET)

What is DOCRET? DOCRET is a Documentation Retrieval MCP Server that enables AI assistants to access up-to-date documentation for various Python libraries, ensuring that AI applications always have access to the latest official documentation. How to use DOCRET? To use DOCRET, clone the repository, set up a virtual environment, install dependencies, configure environment variables, and run the server. You can then use the provided API to fetch documentation content from supported libraries. Key features of DOCRET? Dynamic documentation retrieval for specified Python libraries. Asynchronous web searches using the SERPER API. HTML parsing to extract readable text from documentation. Extensible design to easily add support for additional libraries. Use cases of DOCRET? Fetching the latest documentation for LangChain, LlamaIndex, and OpenAI. Integrating with AI assistants for real-time documentation access. Supporting developers in accessing up-to-date library information. FAQ from DOCRET? What libraries does DOCRET support? Currently, DOCRET supports LangChain, LlamaIndex, and OpenAI, with plans to add more. Is there a cost to use DOCRET? No, DOCRET is open-source and free to use. How can I extend DOCRET to support more libraries? You can update the docs_urls dictionary in main.py with the new library name and its documentation URL.

As an MCP (Model Context Protocol) server, Documentation Retrieval MCP Server (DOCRET) enables AI agents to communicate effectively through standardized interfaces. The Model Context Protocol simplifies integration between different AI models and agent systems.

To use DOCRET, clone the repository, set up a virtual environment, install dependencies, configure environment variables, and run the server. You can then use the provided API to fetch documentation content from supported libraries. Key features of DOCRET? Dynamic documentation retrieval for specified Python libraries. Asynchronous web searches using the SERPER API. HTML parsing to extract readable text from documentation. Extensible design to easily add support for additional libraries. Use cases of DOCRET? Fetching the latest documentation for LangChain, LlamaIndex, and OpenAI. Integrating with AI assistants for real-time documentation access. Supporting developers in accessing up-to-date library information. FAQ from DOCRET? What libraries does DOCRET support? Currently, DOCRET supports LangChain, LlamaIndex, and OpenAI, with plans to add more. Is there a cost to use DOCRET? No, DOCRET is open-source and free to use. How can I extend DOCRET to support more libraries? You can update the docs_urls dictionary in main.py with the new library name and its documentation URL.

Learn how to integrate this MCP server with your AI agents and leverage the Model Context Protocol for enhanced capabilities.