firecrawl
Overview
The firecrawl-mcp-server is a Model Context Protocol (MCP) server that enables AI assistants to crawl, scrape, and extract content from websites at scale using Firecrawl. It allows AI-driven workflows to turn entire websites — including multi-page documentation, blogs, and knowledge bases — into clean, structured, machine-readable data without building or maintaining custom crawlers.
This server is especially useful for research, content ingestion, documentation analysis, and retrieval-augmented generation (RAG) workflows that rely on high-quality web content.
Transport
stdio
Tools
Key Capabilities
- Full-site crawling — Ingest entire websites, not just single pages.
- Clean content extraction — Strip boilerplate and return readable, structured text.
- Scalable web ingestion — Handle large documentation sites and content collections reliably.
- RAG-ready outputs — Produce content that can be indexed into vector databases or search systems.
- Reduced scraping complexity — Abstract away crawling logic, retries, and site structure handling.
How It Works
The firecrawl-mcp-server runs as an MCP service that acts as a bridge between AI clients and Firecrawl’s managed crawling infrastructure. When an AI assistant requests website content, the server orchestrates crawling and extraction across the target site and returns structured results over the MCP protocol.
Authentication is handled using a Firecrawl API key, while crawling behavior (such as depth, scope, and limits) is managed by the server. The returned content is normalized into formats that AI assistants can reason over directly or pass along to downstream systems like vector databases, search indexes, or document stores.
By centralizing crawling and extraction behind MCP, this design allows AI workflows to reliably ingest large amounts of web content without embedding brittle scraping logic, making Firecrawl a powerful foundation for knowledge ingestion and research pipelines.