What is an MCP server for web scraping?

An MCP server for web scraping is a process that implements the Model Context Protocol and exposes web-fetching tools (for AgentFetch: fetch_url, estimate_tokens, fetch_multiple, and search_and_fetch) to AI agents like Claude Desktop, Cursor, and Cline. Instead of writing a custom Python scraping script for every agent, you add one line to your MCP client config and the model gets a typed tool it can call autonomously. The protocol, open-sourced by Anthropic in late 2024, has become the de-facto plug-in standard for AI agents, with public servers catalogued in directories such as Glama, PulseMCP, and mcp.so. AgentFetch is a scraping-focused MCP server that natively handles markdown conversion, source routing (Trafilatura → Jina → FireCrawl → PDF), six-hour caching, and JSON output via the format parameter of fetch_url. Other categories of MCP server exist for databases, filesystems, APIs, and SaaS tools, but the web-scraping subcategory is critical because almost every real agent task — research, monitoring, lead gen, RAG ingestion — needs current web data. Compared to writing a custom HTTP client per agent, an MCP scraping server gives you reusable tooling, consistent error handling, and shared rate-limit budgets across agents.