Read Website Fast MCP Server

The read‑website-fast MCP server tackles a common bottleneck in AI‑driven web interaction: the slow, token‑heavy process of fetching and parsing full HTML pages. Traditional crawlers often pull entire documents into memory, strip noise with expensive regexes, and then feed the raw markup to language models. This not only stalls development cycles but also forces LLMs to waste tokens on irrelevant content. By contrast, read‑website-fast downloads pages locally, applies Mozilla’s Readability engine to isolate the core article content, and immediately converts that clean HTML into Markdown. The result is a lightweight, semantically rich representation that preserves headings, lists, images, and links—all while keeping the token count to a minimum.

For developers building AI assistants or IDE integrations, this server offers several tangible benefits. First, the startup time is minimal thanks to lazy loading of the MCP SDK, so your assistant can invoke web reads on demand without a noticeable lag. Second, the conversion pipeline (Readability → Turndown with GFM support) guarantees that the Markdown output is readable by humans and easily ingestible by downstream NLP components. Third, built‑in caching using SHA‑256 hashes of URLs means repeated requests for the same page are served from disk, eliminating redundant network traffic and further reducing token usage. Finally, polite crawling with robots.txt awareness and rate limiting ensures the server respects site policies, making it safe for production use.

Key capabilities include concurrent fetching with configurable depth, allowing a single call to retrieve an article and its linked subpages up to a user‑defined limit. The server also streams results as they arrive, keeping memory usage low even for large sites. Link preservation is intentional: every outbound URL is retained in the Markdown, enabling downstream tools to build knowledge graphs or follow up on related content. Optional chunking can be enabled for pipelines that require splitting documents into smaller segments before further processing.

Real‑world scenarios where read‑website-fast shines are plentiful. A developer in an IDE can quickly pull up the documentation of a library by simply typing a command, and the assistant will return a clean Markdown snippet ready for editing or summarization. An LLM pipeline that builds contextual knowledge bases can use the server to harvest news articles, forum posts, or product pages without bloating the prompt. Even conversational agents that need to browse the web on demand—such as those in customer support or research assistants—can benefit from the fast, token‑efficient extraction that keeps interactions snappy and cost‑effective.