Mcp Server Webcrawl

Overview

The MCP Server Webcrawl is a specialized search engine that turns raw web‑crawl data into an AI‑friendly knowledge base. It solves the common problem of sifting through millions of archived pages by providing a full‑text, boolean‑enabled search interface that can be queried directly from an LLM. Developers who have already run web crawlers such as ArchiveBox, HTTrack, or WARC can now expose that data to Claude (or any MCP‑compatible assistant) without writing custom parsers or database schemas. The server acts as a bridge, translating crawler output into structured resources that the assistant can filter by type, HTTP status, or other metadata.

At its core, the server offers a menu of search tools that an LLM can invoke on demand. When a user asks for “find all pages containing the phrase ‘privacy policy’,” the assistant sends a query to MCP, which in turn runs a full‑text search across all stored crawl files. The results are returned as Markdown snippets, allowing the assistant to present concise excerpts or even render entire pages if needed. Because the search logic is encapsulated in MCP, developers can focus on higher‑level prompts rather than low‑level query syntax.

Key capabilities include multi‑crawler compatibility, allowing the same API to work with ArchiveBox, HTTrack, Katana, and others. Filters for content type (HTML, PDF, JSON), HTTP status codes, and crawl timestamps give the LLM fine‑grained control over what it retrieves. Boolean search support lets users combine conditions—e.g., “(product page OR landing page) AND NOT 404”—directly in the prompt. The server also supports Markdown rendering and snippet extraction, making it easy to embed search results in conversational outputs.

Real‑world use cases abound. A marketing team can run an SEO audit by prompting the assistant to search for missing meta tags across a site. A security analyst might use the 404 audit routine to locate broken links that could expose sensitive data. Researchers can quickly pull excerpts from archived news articles for citation or trend analysis. Because the server exposes a simple, declarative interface, these workflows can be scripted as prompt routines and reused across projects.

Integration into AI pipelines is seamless: the MCP server registers itself with Claude Desktop, exposing a set of tools that appear in the assistant’s menu. Once configured, any prompt can reference the server by name, and the LLM will automatically translate natural‑language requests into structured queries. The result is a powerful, low‑code method for turning static crawl data into dynamic, AI‑driven insights.