DocsFetcher MCP Server

DocsFetcher MCP Server is a purpose‑built bridge that brings package documentation from the world’s major programming ecosystems directly into large language model workflows. Developers no longer need to manually browse npm, PyPI, Maven Central, or other registries; the server accepts a simple package name or URL and returns structured data that Claude, Cursor IDE, or any MCP‑compatible client can consume instantly. By eliminating the need for API keys and handling the intricacies of each ecosystem’s documentation format, DocsFetcher solves a recurring pain point: how to surface up‑to‑date, language‑specific docs within an AI assistant without opening a browser.

At its core, the server offers four versatile tools. The fetch‑package-docs tool pulls all public-facing information—README, API references, code snippets, and repository metadata—for a single package. fetch‑url-docs allows callers to target any documentation URL, making it possible to fetch docs from custom sites or internal repositories. fetch‑library-docs intelligently decides whether a name or URL was supplied and dispatches the appropriate fetcher, while fetch‑multilingual-docs extends this logic across multiple ecosystems simultaneously. These tools expose a unified, JSON‑structured payload that downstream prompts can ingest for summarization or error explanation.

The server’s prompt suite complements the tools by providing ready‑made conversational hooks. summarize-library-docs generates concise, human‑readable overviews suitable for onboarding or quick reference. explain-dependency-error turns obscure package‑resolution failures into clear, actionable explanations—an invaluable aid when troubleshooting complex dependency graphs. Together, tools and prompts turn raw documentation into curated knowledge that an LLM can present in natural language or code‑ready snippets.

DocsFetcher shines in real‑world scenarios where rapid iteration across languages is required. A full‑stack engineer can ask, “Show me the documentation for lodash in JavaScript and pandas in Python” and receive side‑by‑side summaries, or a data scientist can request a comparative overview of pandas versus R’s data.table. In continuous‑integration pipelines, the server can be invoked to fetch the latest docs for a dependency before generating changelogs or release notes. Because it requires no API keys, teams can embed it in internal tooling, IDE extensions, or even on-premise LLM deployments without exposing secrets.

By integrating seamlessly with Claude Desktop and Cursor IDE through simple MCP configuration, DocsFetcher becomes a first‑class citizen in any AI‑augmented development workflow. Its unique combination of multi‑ecosystem support, zero‑credential operation, and structured output gives developers a powerful, low‑friction way to keep their codebases informed by the latest library documentation—all within the conversational context of an AI assistant.