mcpField
F
Firecrawl MCP Server

Firecrawl MCP Server

Local
github.com/MspariharBrowser Automation3Updated 89w ago

About Firecrawl MCP Server

Firecrawl MCP Server makes Firecrawl API capabilities for scraping, searching, crawling, and structured data extraction available to MCP clients handling web content. It also supports site mapping, content filtering, and multiple output formats.

Key features

  1. 01Scrape pages and filter content
  2. 02Search web content in multiple languages
  3. 03Crawl sites with depth controls
  4. 04Generate site structure maps
  5. 05Extract structured data from multiple URLs

Use cases

  1. 01Convert webpage content to Markdown
  2. 02Crawl a site by path and depth
  3. 03Batch-extract structured information from pages

MCP support

The MCP server exposes Firecrawl API tools for scraping, search, crawling, site mapping, and data extraction; a Firecrawl API key is required.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Firecrawl MCP Server

A Model Context Protocol (MCP) server for web scraping, content searching, site crawling, and data extraction using the Firecrawl API.

Features

  • Web Scraping: Extract content from any webpage with customizable options

    • Mobile device emulation
    • Ad and popup blocking
    • Content filtering
    • Structured data extraction
    • Multiple output formats
  • Content Search: Intelligent search capabilities

    • Multi-language support
    • Location-based results
    • Customizable result limits
    • Structured output formats
  • Site Crawling: Advanced web crawling functionality

    • Depth control
    • Path filtering
    • Rate limiting
    • Progress tracking
    • Sitemap integration
  • Site Mapping: Generate site structure maps

    • Subdomain support
    • Search filtering
    • Link analysis
    • Visual hierarchy
  • Data Extraction: Extract structured data from multiple URLs

    • Schema validation
    • Batch processing
    • Web search enrichment
    • Custom extraction prompts

Installation

# Global installation
npm install -g @modelcontextprotocol/mcp-server-firecrawl

# Local project installation
npm install @modelcontextprotocol/mcp-server-firecrawl

Quick Start

  1. Get your Firecrawl API key from the developer portal

  2. Set your API key:

    Unix/Linux/macOS (bash/zsh):

    export FIRECRAWL_API_KEY=your-api-key
    

    Windows (Command Prompt):

    set FIRECRAWL_API_KEY=your-api-key
    

    Windows (PowerShell):

    $env:FIRECRAWL_API_KEY = "your-api-key"
    

    Alternative: Using .env file (recommended for development):

    # Install dotenv
    npm install dotenv
    
    # Create .env file
    echo "FIRECRAWL_API_KEY=your-api-key" > .env
    

    Then in your code:

    import dotenv from 'dotenv';
    dotenv.config();
    
  3. Run the server:

    mcp-server-firecrawl
    

Integration

Claude Desktop App

Add to your MCP settings:

{
  "firecrawl": {
    "command": "mcp-server-firecrawl",
    "env": {
      "FIRECRAWL_API_KEY": "your-api-key"
    }
  }
}
Claude VSCode Extension

Add to your MCP configuration:

{
  "mcpServers": {
    "firecrawl": {
      "command": "mcp-server-firecrawl",
      "env": {
        "FIRECRAWL_API_KEY": "your-api-key"
      }
    }
  }
}

Usage Examples

Web Scraping
// Basic scraping
{
  name: "scrape_url",
  arguments: {
    url: "https://example.com",
    formats: ["markdown"],
    onlyMainContent: true
  }
}

// Advanced extraction
{
  name: "scrape_url",
  arguments: {
    url: "https://example.com/blog",
    jsonOptions: {
      prompt: "Extract article content",
      schema: {
        title: "string",
        content: "string"
      }
    },
    mobile: true,
    blockAds: true
  }
}
Site Crawling
// Basic crawling
{
  name: "crawl",
  arguments: {
    url: "https://example.com",
    maxDepth: 2,
    limit: 100
  }
}

// Advanced crawling
{
  name: "crawl",
  arguments: {
    url: "https://example.com",
    maxDepth: 3,
    includePaths: ["/blog", "/products"],
    excludePaths: ["/admin"],
    ignoreQueryParameters: true
  }
}
Site Mapping
// Generate site map
{
  name: "map",
  arguments: {
    url: "https://example.com",
    includeSubdomains: true,
    limit: 1000
  }
}
Data Extraction
// Extract structured data
{
  name: "extract",
  arguments: {
    urls: ["https://example.com/product1", "https://example.com/product2"],
    prompt: "Extract product details",
    schema: {
      name: "string",
      price: "number",
      description: "string"
    }
  }
}

Configuration

See configuration guide for detailed setup options.

API Documentation

See API documentation for detailed endpoint specifications.

Development

# Install dependencies
npm install

# Build
npm run build

# Run tests
npm test

# Start in development mode
npm run dev

Examples

Check the examples directory for more usage examples:

Error Handling

The server implements robust error handling:

  • Rate limiting with exponential backoff
  • Automatic retries
  • Detailed error messages
  • Debug logging

Security

  • API key protection
  • Request validation
  • Domain allowlisting
  • Rate limiting
  • Safe error messages

Contributing

See CONTRIBUTING.md for contribution guidelines.

License

MIT License - see LICENSE for details.

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.