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Fetch web pages, extract clean Markdown, cache results, and search reading history.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "scout" yet — see the docs or source repo.
Fetch this webpage and return cleaned Markdown, preserving headings and body structure as much as possible: https://example.com
Returns cleaned Markdown from the webpage, making it easier to read, quote, or pass to an LLM.
Open this link and extract a compact Markdown version suitable for LLM processing, avoiding irrelevant navigation content: https://example.com/article
Outputs a more compact body-focused version of the page, reducing noise and token usage.
Search the previously read page history and find pages about “vector databases.”
Returns matching items from reading history so previously fetched pages can be found quickly.
Researchers, developers, or writers can fetch a webpage and convert it into clean Markdown before sending it to an LLM. This reduces irrelevant page elements and lowers token usage.
When users need to find material they viewed earlier, they can use searchable reading history to locate relevant pages quickly. This is useful for research backtracking and source lookup.
If the same webpage is accessed multiple times, caching can reduce repeated fetch overhead. This fits workflows that repeatedly analyze the same set of pages.
It is an MCP server for fetching web pages, extracting clean Markdown, caching results, and providing searchable reading history. It is useful for preparing web content for AI or human reading.
Based on the description, it does more than fetch pages: it extracts cleaner Markdown to reduce token count, and it adds caching plus searchable reading history. It is more focused on AI-ready reading and knowledge recall than simply downloading raw pages.
The provided information does not include installation steps, runtime requirements, or API key needs. Please check the source repository for details.
Scrape single or multiple web pages into clean Markdown for reuse.
Fetch web pages as Markdown and answer questions about their content.
Scrape web pages into Markdown for content capture and agent workflows.
Fetch web pages as markdown for LLM reading, analysis, and automation.
Give AI agents keyless web search for fast online information retrieval.
Local-first MCP server for web search and developer documentation fetching.