Fetch
Web content fetching and conversion for efficient LLM usage.
Details
Category
Reference Servers
Implementation Language
TypeScript
Tags
Resources
GitHub Repository
https://github.com/modelcontextprotocol/servers/blob/main/src/fetchDeep Review
The Fetch MCP server provides web content retrieval and conversion capabilities optimized for AI consumption. It fetches web pages, extracts clean content, converts to markdown, and handles various content types including HTML, PDF, and structured data. This server is essential for AI assistants that need to access and understand web content efficiently.
Core Capabilities
Fetch server retrieves web content via HTTP/HTTPS, extracts main content while removing boilerplate (ads, navigation, footers), converts HTML to clean markdown for better AI processing, handles redirects and common web patterns, respects robots.txt and rate limiting, and supports custom headers and authentication. It includes built-in content cleaning algorithms that preserve semantic structure while removing noise.
Use Cases
Research and information gathering from web sources, content analysis and summarization tasks, documentation retrieval for technical questions, news and article processing for AI assistants, web scraping for structured data extraction, and competitive intelligence gathering. The server excels at converting messy web content into clean, AI-friendly formats.
Setup and Configuration
Install with 'npx -y @modelcontextprotocol/server-fetch'. Basic usage requires no configuration. Advanced options include custom user agents, request timeouts, max content length limits, allowed/blocked domains, proxy configuration, and custom header injection. Configure rate limiting to respect target servers and avoid being blocked.
Best Practices
Always respect robots.txt and site terms of service. Implement caching to avoid repeated requests for the same content. Set reasonable timeouts (10-30 seconds) to handle slow sites. Use domain allowlists for sensitive applications. Include proper user agent strings identifying your application. Handle errors gracefully as web content is inherently unreliable. Consider implementing retry logic with exponential backoff.
Examples
Fetch and convert article
Input: URL: 'https://example.com/article'
Expected: Returns clean markdown with title, content, and metadata. Removes ads, navigation, and other boilerplate. Preserves article structure and formatting.
Extract structured data
Input: URL: 'https://example.com/product' with schema extraction
Expected: Returns product information in structured format including title, price, description, and availability
Comparisons
Puppeteer server
Pros: Handles JavaScript-heavy sites; full browser capabilities
Cons: Much slower; higher resource usage; more complex setup
Direct HTTP requests
Pros: Simple; fast
Cons: No content cleaning; requires manual parsing; no markdown conversion
Conclusion
Fetch server is the go-to solution for web content retrieval in MCP environments. Its content cleaning and markdown conversion make web data immediately useful for AI processing. Essential for any AI assistant that needs to access web information.