DocReader MCP Server

DocReader MCP Tool – A One‑Stop Documentation Assistant

DocReader is an MCP server designed to bridge the gap between large language models and structured, web‑based documentation. By exposing a small set of intuitive tools—searching, extracting, summarizing, and performing the entire workflow in one call—it lets AI assistants answer user questions with up‑to‑date, contextually relevant information directly from a target documentation site. This eliminates the need for developers to manually curate or parse docs, saving time and reducing errors.

The server tackles a common pain point: LLMs often lack reliable access to external knowledge bases. DocReader resolves this by leveraging standard HTTP requests and HTML parsing libraries to crawl a documentation website, locate pages that match a query, pull the relevant text, and condense it into a concise answer. Developers can therefore build assistants that stay current with the latest API changes, tutorials, or policy updates without redeploying the model.

Key capabilities are wrapped in four straightforward functions:

• search_docs scans a documentation site for pages most relevant to a user query, returning URLs and snippets that guide the assistant’s focus.
• extract_content pulls the raw text from a specific page, optionally filtering by keyword or section.
• summarize_findings takes the collected passages and produces a human‑readable summary that highlights actionable insights.
• read_doc chains the three steps above, delivering a complete answer in one request.

These tools are valuable because they operate entirely within the MCP framework, making integration seamless for any client that already speaks MCP. A developer can add DocReader as a local or remote tool, invoke it from an AI workflow in Cursor, or call it directly via fastmcp’s CLI. The server’s design promotes composability: a conversational agent can first ask the user for clarification, then use to narrow down pages, and finally present a synthesized response powered by .

Real‑world scenarios include building support bots for SDK documentation, creating interactive learning assistants that pull examples from official tutorials, or automating compliance checks against policy documents. In each case, DocReader eliminates manual lookup and ensures that answers reflect the most recent version of the source material. Its lightweight Python stack (BeautifulSoup, requests, OpenAI) keeps deployment straightforward while offering the flexibility to swap in other LLM providers if needed.

Overall, DocReader exemplifies how MCP can empower AI assistants to interact intelligently with external knowledge sources. By encapsulating the search‑extract‑summarize cycle in a single, well‑defined protocol, it gives developers a robust tool for turning static documentation into dynamic, conversational knowledge bases.