Scrapling Fetch MCP

Overview

scrapling‑fetch‑mcp is a Model Context Protocol (MCP) server designed to give AI assistants like Claude the ability to retrieve and analyze text content from web pages that employ bot‑detection mechanisms. In many real‑world scenarios developers need up‑to‑date documentation, API references, or specific snippets from a site that blocks automated requests. Traditional scraping tools either fail against the protection layers or require complex setups, leaving the AI unable to fetch the information it needs. This MCP server bridges that gap by providing a lightweight, low‑volume solution that works seamlessly within the AI workflow.

The server exposes two primary tools that Claude can invoke automatically: Page fetching and Pattern extraction. When a user asks for “the docs at https://example.com/api” or “find all mentions of ‘authentication’ on that page,” the assistant selects the appropriate tool without manual intervention. Page fetching returns a full HTML document, including pagination support for long articles or multi‑page documentation. Pattern extraction allows the assistant to locate and pull out specific pieces of text using regular expressions, enabling precise retrieval of installation instructions or configuration snippets.

Key capabilities include a tiered bot‑detection bypass: basic (fast, 1–2 s), stealth (moderate, 3–8 s), and max‑stealth (slow, 10+ s). Claude automatically starts with the fastest mode and escalates only if necessary, optimizing response time while maintaining reliability across sites that use Cloudflare or similar protections. The MCP server returns rich metadata—such as content length and page structure—that informs the assistant’s decision‑making process, allowing it to decide whether to continue paging or focus on a specific pattern.

Typical use cases span documentation automation, knowledge‑base updates, and compliance checks. For example, a developer can ask Claude to pull the latest installation guide from a vendor’s site and embed it into internal onboarding materials. In another scenario, security teams can request the most recent vulnerability disclosures from a protected portal and have them automatically parsed into a ticketing system. Because the tool is designed for low‑volume, text‑only content, it stays lightweight and avoids triggering anti‑scraping defenses that could block broader data harvesting attempts.

By integrating scrapling‑fetch‑mcp into an AI workflow, developers gain a reliable, privacy‑respectful method to fetch protected web content on demand. The server’s automatic tool selection and adaptive bypass levels make it a standout solution for any project that requires up‑to‑date, site‑specific information without the overhead of managing complex scraping infrastructure.