Pragmar MCP Server Webcrawl

The mcp-server-webcrawl server bridges the gap between raw web‑crawled data and conversational AI models. By exposing a Model Context Protocol interface, it lets assistants such as Claude or future ChatGPT clients retrieve, filter, and analyze content that has already been collected by a variety of web crawlers. This eliminates the need for the AI to perform expensive network requests or parse raw HTML on‑the‑fly, giving developers a fast, consistent source of structured information that can be queried directly from the assistant’s context.

At its core, the server provides a full‑text search engine with Boolean query support and rich filtering options—by resource type, HTTP status code, crawler origin, or any custom metadata embedded in the crawl. Developers can point the server at archives produced by WARC, wget mirrors, InterroBot databases, Katana text caches, or SiteOne archives. Once the data source is configured, the assistant can issue natural‑language queries that are translated into efficient search requests against the underlying index. The server then returns ranked results, snippets, or metadata that can be fed back into the conversation, enabling tasks like fact‑checking, summarization of recent news, or extraction of policy documents from a corporate intranet.

Key capabilities include:

• Multi‑crawler compatibility – a single MCP interface works with any supported crawler, simplifying infrastructure and reducing maintenance overhead.
• Fine‑grained filtering – developers can restrict results to specific MIME types, status codes, or crawler origins, ensuring that the assistant only considers relevant documents.
• Boolean search and relevance ranking – complex queries can be expressed in natural language, while the server handles efficient full‑text indexing and scoring.
• Quick MCP configuration – integration with Claude Desktop’s settings panel allows users to add or remove server instances without editing code, making it accessible for both developers and non‑technical stakeholders.

Real‑world use cases span from internal knowledge bases to compliance audits. A legal team could query a web crawl of regulatory filings, while a marketing department might search recent competitor site changes. Because the server returns structured data rather than raw HTML, downstream pipelines can perform summarization, entity extraction, or sentiment analysis with minimal latency. Its open‑source nature and reliance on standard Python tooling make it easy to embed in existing CI/CD workflows, ensuring that AI assistants always have up‑to‑date context from the web without compromising security or performance.