Docs Fetch MCP Server

A Model Context Protocol (MCP) server for fetching web content with recursive exploration capabilities. This server enables LLMs to autonomously explore web pages and documentation to learn about specific topics.

Overview

The Docs Fetch MCP Server provides a simple but powerful way for LLMs to retrieve and explore web content. It enables:

Fetching clean, readable content from any web page
Recursive exploration of linked pages up to a specified depth
Same-domain link traversal to gather comprehensive information
Smart filtering of navigation links to focus on content-rich pages

This tool is particularly useful when users want an LLM to learn about a specific topic by exploring documentation or web content.

Related MCP server: Fetch MCP Server

Features

Content Extraction: Cleanly extracts the main content from web pages, removing distractions like navigation, ads, and irrelevant elements
Link Analysis: Identifies and extracts links from the page, assessing their relevance
Recursive Exploration: Follows links to related content within the same domain, up to a specified depth
Parallel Processing: Efficiently crawls content with concurrent requests and proper error handling
Robust Error Handling: Gracefully handles network issues, timeouts, and malformed pages
Dual-Strategy Approach: Uses fast axios requests first with puppeteer as a fallback for more complex pages
Timeout Prevention: Implements global timeout handling to ensure reliable operation within MCP time limits
Partial Results: Returns available content even when some pages fail to load completely

Usage

The server exposes a single MCP tool:

`fetch_doc_content`

Fetches web page content with the ability to explore linked pages up to a specified depth.

Parameters:

url (string, required): URL of the web page to fetch
depth (number, optional, default: 1): Maximum depth of directory/link exploration (1-5)

Returns:

{ "rootUrl": "https://example.com/docs", "explorationDepth": 2, "pagesExplored": 5, "content": [ { "url": "https://example.com/docs", "title": "Documentation", "content": "Main page content...", "links": [ { "url": "https://example.com/docs/topic1", "text": "Topic 1" }, ... ] }, ... ] }