Docret MCP Server

Overview

The Documentation Retrieval MCP Server (DOCRET) solves a common pain point for developers building AI‑powered assistants: staying current with the ever‑evolving documentation of popular Python libraries. When an assistant must answer a question about LangChain, LlamaIndex, or OpenAI, it needs reliable, up‑to‑date references. DOCRET provides a dedicated MCP endpoint that automatically fetches the latest documentation pages, parses them into readable text, and returns the content to the client. This eliminates manual copy‑and‑paste of docs or reliance on static snapshots that quickly become stale.

At its core, DOCRET acts as a bridge between an AI assistant and the web. It leverages the SERPER API to perform targeted Google searches confined to a library’s official site, ensuring that results are relevant and authoritative. Once a URL is identified, the server scrapes the page with BeautifulSoup, stripping HTML clutter and extracting plain text. The extracted snippets are then packaged into the MCP response format so that an assistant can seamlessly incorporate them into its replies. The asynchronous design allows multiple queries to run concurrently, keeping latency low even under load.

Key capabilities include:

• Dynamic retrieval of documentation for any supported library, guaranteeing that assistants reference the newest API changes or feature updates.
• Asynchronous web searching via SERPER, which is faster and more reliable than generic crawling.
• HTML parsing that normalizes content across different sites, delivering clean, human‑readable text.
• Extensibility: adding a new library only requires updating a configuration dictionary, making the server adaptable to future needs.

Real‑world scenarios where DOCRET shines include building a coding tutor that must explain recent LangChain API changes, creating a chatbot that assists data scientists with LlamaIndex queries, or integrating an AI assistant into an IDE to provide instant documentation lookup without leaving the editor. In each case, the assistant can request a snippet on demand, receive accurate information, and present it to the user in context.

Integrating DOCRET into an AI workflow is straightforward: the assistant’s MCP client lists the server in its configuration, and when a user asks for documentation, the client invokes the function with the query and library name. The server handles search, scrape, and response formatting behind the scenes, allowing developers to focus on higher‑level conversational logic. DOCRET’s tight coupling with MCP means it can be paired with any compliant client—Claude, OpenAI models, or custom agents—making it a versatile tool for modern AI development pipelines.