Crawl4ai MCP Server

What is Crawl4ai MCP Server?

A MCP server that provides web crawling capabilities using crawl4ai, returning markdown content with citations for use by LLMs. It is for developers building AI applications that need to fetch and structure web page content via the Model Context Protocol.

How to use Crawl4ai MCP Server?

Install dependencies with npm install, build with npm run build, then add the server configuration to your MCP client environment, setting CRAWL4AI_API_URL (optional) and CRAWL4AI_AUTH_TOKEN (optional). Invoke the crawl_urls tool with a list of URLs to crawl.

Key features of Crawl4ai MCP Server

• Single tool crawl_urls accepts a list of URLs
• Returns markdown content with numbered citations per URL
• Multiple URLs are separated by horizontal rules
• Automatically handles retries with different user agents
• Requires a running crawl4ai API instance (Docker deployment)

Use cases of Crawl4ai MCP Server

• Providing up-to-date web content as context for LLM prompts
• Scraping documentation or articles for summarization
• Aggregating content from multiple URLs into a single markdown response

FAQ from Crawl4ai MCP Server

What is crawl4ai and do I need it?

Crawl4ai is an external web crawling service. You must have a running crawl4ai instance, typically deployed via Docker, for this server to work.

What are the runtime requirements?

Node.js and a running crawl4ai API instance. No other dependencies are needed beyond the npm packages.

Where does the web page data live?

The server does not store data; it fetches content from each URL via the crawl4ai API and returns it directly to the MCP client.

Are there any known limits or errors?

Timeouts may occur with too many URLs per request. The service handles retries automatically, but you can reduce the number of URLs if problems persist.

What transport and authentication are used?

The server uses standard I/O transport for MCP. Authentication to the crawl4ai API is optional via the CRAWL4AI_AUTH_TOKEN environment variable.