Fetch MCP Server

Overview

The Fetch MCP Server bridges the gap between AI assistants and live web data by providing a simple, on‑demand interface for retrieving and transforming content from any URL. Instead of embedding custom scraping logic into your assistant, you can delegate the task to this server and receive clean, well‑structured data that can be directly consumed by downstream prompts or tools.

At its core, the server offers four distinct “fetch” tools—, , , and . Each tool accepts a URL, optional custom headers, and pagination parameters ( and ) that allow large documents to be streamed in chunks. The response format matches the tool’s purpose: raw HTML, parsed JSON objects, plain text stripped of markup, or Markdown‑converted content. This flexibility lets developers choose the representation that best fits their workflow: a conversational agent might prefer Markdown for readability, while an analytical pipeline may need raw JSON.

Key features include:

• Header customization for authenticated or specialized requests, enabling access to protected APIs or tailored content.
• Chunked fetching via and , which prevents memory overload when dealing with lengthy pages or large JSON payloads.
• Built‑in parsing using JSDOM to extract text and TurndownService for reliable HTML‑to‑Markdown conversion, ensuring that the output is clean and consistent.
• Zero persistence; the server never stores fetched data, aligning with privacy best practices and keeping resource usage minimal.

Typical use cases span a wide range of AI‑powered applications:

• Information retrieval: An assistant can pull the latest news article, strip extraneous HTML, and present a concise summary.
• Data ingestion: A chatbot that aggregates product listings can fetch JSON feeds and feed them into a knowledge base.
• Content transformation: A content creation tool can convert blog posts to Markdown, ready for further editing or publishing.
• Testing and debugging: Developers can quickly verify that their AI workflows correctly handle different content types without writing custom fetch logic.

Integration into an MCP‑enabled workflow is straightforward. The server exposes its tools via standard MCP interfaces, so any client that understands the protocol—be it a web app, desktop assistant, or serverless function—can invoke the appropriate tool with minimal boilerplate. By offloading web fetching to this dedicated service, developers can focus on higher‑level logic and user experience while ensuring reliable, scalable access to external data.