Fetch MCP Server

What the Fetch MCP server does

How models use it and what it is built for.

The fetch server retrieves a single URL and simplifies the result before the model ever sees it, converting HTML into markdown so a page costs a fraction of the tokens raw markup would. Long pages are handled with a start_index cursor and a max_length cap: the model reads a chunk, and if the response was truncated it calls again from where it stopped, which lets it work through a long article without blowing the context window. A raw flag returns unconverted content when you actually need the markup. By default the server honours the target site robots.txt for model-initiated requests and identifies itself with an MCP user agent; both behaviours are configurable, and the server distinguishes requests the model made on its own from ones a user explicitly asked for.

Tools the Fetch MCP server exposes

Typical tools an AI model can call. Exact names vary by version.

• fetch — retrieve a URL and return its content as markdown
• url — the target address, the only required argument
• max_length — cap the characters returned so a long page cannot flood context
• start_index — resume from a character offset to page through long documents
• raw — skip the markdown conversion and return the content as served

Connecting to Fetch

Taken from the official Fetch documentation — see Fetch MCP Server — official reference implementation for the full reference.

{
  "mcpServers": {
    "fetch": {
      "command": "uvx",
      "args": ["mcp-server-fetch"]
    }
  }
}

{
  "mcpServers": {
    "fetch": {
      "command": "uvx",
      "args": ["mcp-server-fetch", "--ignore-robots-txt"]
    }
  }
}

Example prompts to try

Copy any of these into MCP Agent Studio after connecting.

• Read this changelog URL and tell me what changed in the last two releases.

• Fetch this RFC and summarise the sections that concern authentication.

• Open this blog post and pull out every code block.

• Read this long documentation page in chunks and list the configuration options.

Models on MCP Playground

This is not a single-model product: you get the same MCP connection with 60+ models (Claude, GPT, Gemini, DeepSeek, open-weight, and more), you can switch mid-conversation, and you can open Compare mode to run the same prompt against multiple models at once. The card above is a suggested starting point for this server — not the only choice.

Check an AI agent can actually use the Fetch MCP server

Listing tools proves the server is reachable, not that a model can work with it. Evals go further: they read every tool on the server, write a test suite from its real schemas, and run it — code decides pass/fail on the responses (schema conformance, error codes, pagination, result caps) while a scoring model grades plain-English tasks driven through the tools.

Fetch MCP server — FAQ

Common questions about connecting, scoping and using it safely.