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GitHub - mendableai/firecrawl-mcp-server: 🔥 Official Firecrawl MCP Server

A Model Context Protocol (MCP) server implementation that integrates with Firecrawl for web scraping capabilities.

Big thanks to @vrknetha, @knacklabs for the initial implementation!

• Web scraping, crawling, and discovery
• Search and content extraction
• Deep research and batch scraping
• Automatic retries and rate limiting
• Cloud and self-hosted support
• SSE support

Play around with our MCP Server on MCP.so's playground or on Klavis AI.

env FIRECRAWL_API_KEY=fc-YOUR_API_KEY npx -y firecrawl-mcp

npm install -g firecrawl-mcp

Configuring Cursor 🖥️ Note: Requires Cursor version 0.45.6+ For the most up-to-date configuration instructions, please refer to the official Cursor documentation on configuring MCP servers: Cursor MCP Server Configuration Guide

To configure Firecrawl MCP in Cursor v0.48.6

1. Open Cursor Settings
2. Go to Features > MCP Servers
3. Click "+ Add new global MCP server"
4. Enter the following code:

   {
     "mcpServers": {
       "firecrawl-mcp": {
         "command": "npx",
         "args": ["-y", "firecrawl-mcp"],
         "env": {
           "FIRECRAWL_API_KEY": "YOUR-API-KEY"
         }
       }
     }
   }

To configure Firecrawl MCP in Cursor v0.45.6

1. Open Cursor Settings
2. Go to Features > MCP Servers
3. Click "+ Add New MCP Server"
4. Enter the following:
   • Name: "firecrawl-mcp" (or your preferred name)
   • Type: "command"
   • Command: env FIRECRAWL_API_KEY=your-api-key npx -y firecrawl-mcp

If you are using Windows and are running into issues, try cmd /c "set FIRECRAWL_API_KEY=your-api-key && npx -y firecrawl-mcp"

Replace your-api-key with your Firecrawl API key. If you don't have one yet, you can create an account and get it from https://www.firecrawl.dev/app/api-keys

After adding, refresh the MCP server list to see the new tools. The Composer Agent will automatically use Firecrawl MCP when appropriate, but you can explicitly request it by describing your web scraping needs. Access the Composer via Command+L (Mac), select "Agent" next to the submit button, and enter your query.

Add this to your ./codeium/windsurf/model_config.json:

{
  "mcpServers": {
    "mcp-server-firecrawl": {
      "command": "npx",
      "args": ["-y", "firecrawl-mcp"],
      "env": {
        "FIRECRAWL_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

To run the server using Server-Sent Events (SSE) locally instead of the default stdio transport:

env SSE_LOCAL=true FIRECRAWL_API_KEY=fc-YOUR_API_KEY npx -y firecrawl-mcp

Use the url: http://localhost:3000/sse

To install Firecrawl for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @mendableai/mcp-server-firecrawl --client claude

For one-click installation, click one of the install buttons below...

For manual installation, add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).

{
  "mcp": {
    "inputs": [
      {
        "type": "promptString",
        "id": "apiKey",
        "description": "Firecrawl API Key",
        "password": true
      }
    ],
    "servers": {
      "firecrawl": {
        "command": "npx",
        "args": ["-y", "firecrawl-mcp"],
        "env": {
          "FIRECRAWL_API_KEY": "${input:apiKey}"
        }
      }
    }
  }
}

Optionally, you can add it to a file called .vscode/mcp.json in your workspace. This will allow you to share the configuration with others:

{
  "inputs": [
    {
      "type": "promptString",
      "id": "apiKey",
      "description": "Firecrawl API Key",
      "password": true
    }
  ],
  "servers": {
    "firecrawl": {
      "command": "npx",
      "args": ["-y", "firecrawl-mcp"],
      "env": {
        "FIRECRAWL_API_KEY": "${input:apiKey}"
      }
    }
  }
}

• FIRECRAWL_API_KEY: Your Firecrawl API key
  • Required when using cloud API (default)
  • Optional when using self-hosted instance with FIRECRAWL_API_URL
• FIRECRAWL_API_URL (Optional): Custom API endpoint for self-hosted instances
  • Example: https://firecrawl.your-domain.com
  • If not provided, the cloud API will be used (requires API key)

• FIRECRAWL_RETRY_MAX_ATTEMPTS: Maximum number of retry attempts (default: 3)
• FIRECRAWL_RETRY_INITIAL_DELAY: Initial delay in milliseconds before first retry (default: 1000)
• FIRECRAWL_RETRY_MAX_DELAY: Maximum delay in milliseconds between retries (default: 10000)
• FIRECRAWL_RETRY_BACKOFF_FACTOR: Exponential backoff multiplier (default: 2)

• FIRECRAWL_CREDIT_WARNING_THRESHOLD: Credit usage warning threshold (default: 1000)
• FIRECRAWL_CREDIT_CRITICAL_THRESHOLD: Credit usage critical threshold (default: 100)

For cloud API usage with custom retry and credit monitoring:

# Required for cloud API
export FIRECRAWL_API_KEY=your-api-key

# Optional retry configuration
export FIRECRAWL_RETRY_MAX_ATTEMPTS=5        # Increase max retry attempts
export FIRECRAWL_RETRY_INITIAL_DELAY=2000    # Start with 2s delay
export FIRECRAWL_RETRY_MAX_DELAY=30000       # Maximum 30s delay
export FIRECRAWL_RETRY_BACKOFF_FACTOR=3      # More aggressive backoff

# Optional credit monitoring
export FIRECRAWL_CREDIT_WARNING_THRESHOLD=2000    # Warning at 2000 credits
export FIRECRAWL_CREDIT_CRITICAL_THRESHOLD=500    # Critical at 500 credits

For self-hosted instance:

# Required for self-hosted
export FIRECRAWL_API_URL=https://firecrawl.your-domain.com

# Optional authentication for self-hosted
export FIRECRAWL_API_KEY=your-api-key  # If your instance requires auth

# Custom retry configuration
export FIRECRAWL_RETRY_MAX_ATTEMPTS=10
export FIRECRAWL_RETRY_INITIAL_DELAY=500     # Start with faster retries

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "mcp-server-firecrawl": {
      "command": "npx",
      "args": ["-y", "firecrawl-mcp"],
      "env": {
        "FIRECRAWL_API_KEY": "YOUR_API_KEY_HERE",

        "FIRECRAWL_RETRY_MAX_ATTEMPTS": "5",
        "FIRECRAWL_RETRY_INITIAL_DELAY": "2000",
        "FIRECRAWL_RETRY_MAX_DELAY": "30000",
        "FIRECRAWL_RETRY_BACKOFF_FACTOR": "3",

        "FIRECRAWL_CREDIT_WARNING_THRESHOLD": "2000",
        "FIRECRAWL_CREDIT_CRITICAL_THRESHOLD": "500"
      }
    }
  }
}

The server includes several configurable parameters that can be set via environment variables. Here are the default values if not configured:

const CONFIG = {
  retry: {
    maxAttempts: 3, // Number of retry attempts for rate-limited requests
    initialDelay: 1000, // Initial delay before first retry (in milliseconds)
    maxDelay: 10000, // Maximum delay between retries (in milliseconds)
    backoffFactor: 2, // Multiplier for exponential backoff
  },
  credit: {
    warningThreshold: 1000, // Warn when credit usage reaches this level
    criticalThreshold: 100, // Critical alert when credit usage reaches this level
  },
};

These configurations control:

1. Retry Behavior

   • Automatically retries failed requests due to rate limits
   • Uses exponential backoff to avoid overwhelming the API
   • Example: With default settings, retries will be attempted at:
     • 1st retry: 1 second delay
     • 2nd retry: 2 seconds delay
     • 3rd retry: 4 seconds delay (capped at maxDelay)

2. Credit Usage Monitoring

   • Tracks API credit consumption for cloud API usage
   • Provides warnings at specified thresholds
   • Helps prevent unexpected service interruption
   • Example: With default settings:
     • Warning at 1000 credits remaining
     • Critical alert at 100 credits remaining

The server utilizes Firecrawl's built-in rate limiting and batch processing capabilities:

• Automatic rate limit handling with exponential backoff
• Efficient parallel processing for batch operations
• Smart request queuing and throttling
• Automatic retries for transient errors

Use this guide to select the right tool for your task:

• If you know the exact URL(s) you want:
  • For one: use scrape
  • For many: use batch_scrape
• If you need to discover URLs on a site: use map
• If you want to search the web for info: use search
• If you want to extract structured data: use extract
• If you want to analyze a whole site or section: use crawl (with limits!)
• If you want to do in-depth research: use deep_research
• If you want to generate LLMs.txt: use generate_llmstxt

Scrape content from a single URL with advanced options.

Best for:

• Single page content extraction, when you know exactly which page contains the information.

Not recommended for:

• Extracting content from multiple pages (use batch_scrape for known URLs, or map + batch_scrape to discover URLs first, or crawl for full page content)
• When you're unsure which page contains the information (use search)
• When you need structured data (use extract)

Common mistakes:

• Using scrape for a list of URLs (use batch_scrape instead).

Prompt Example:

"Get the content of the page at https://example.com."

Usage Example:

{
  "name": "firecrawl_scrape",
  "arguments": {
    "url": "https://example.com",
    "formats": ["markdown"],
    "onlyMainContent": true,
    "waitFor": 1000,
    "timeout": 30000,
    "mobile": false,
    "includeTags": ["article", "main"],
    "excludeTags": ["nav", "footer"],
    "skipTlsVerification": false
  }
}

Returns:

• Markdown, HTML, or other formats as specified.

Scrape multiple URLs efficiently with built-in rate limiting and parallel processing.

Best for:

• Retrieving content from multiple pages, when you know exactly which pages to scrape.

Not recommended for:

• Discovering URLs (use map first if you don't know the URLs)
• Scraping a single page (use scrape)

Common mistakes:

• Using batch_scrape with too many URLs at once (may hit rate limits or token overflow)

Prompt Example:

"Get the content of these three blog posts: [url1, url2, url3]."

Usage Example:

{
  "name": "firecrawl_batch_scrape",
  "arguments": {
    "urls": ["https://example1.com", "https://example2.com"],
    "options": {
      "formats": ["markdown"],
      "onlyMainContent": true
    }
  }
}

Returns:

• Response includes operation ID for status checking:

{
  "content": [
    {
      "type": "text",
      "text": "Batch operation queued with ID: batch_1. Use firecrawl_check_batch_status to check progress."
    }
  ],
  "isError": false
}

Check the status of a batch operation.

{
  "name": "firecrawl_check_batch_status",
  "arguments": {
    "id": "batch_1"
  }
}

Map a website to discover all indexed URLs on the site.

Best for:

• Discovering URLs on a website before deciding what to scrape
• Finding specific sections of a website

Not recommended for:

• When you already know which specific URL you need (use scrape or batch_scrape)
• When you need the content of the pages (use scrape after mapping)

Common mistakes:

• Using crawl to discover URLs instead of map

Prompt Example:

"List all URLs on example.com."

Usage Example:

{
  "name": "firecrawl_map",
  "arguments": {
    "url": "https://example.com"
  }
}

Returns:

• Array of URLs found on the site

Search the web and optionally extract content from search results.

Best for:

• Finding specific information across multiple websites, when you don't know which website has the information.
• When you need the most relevant content for a query

Not recommended for:

• When you already know which website to scrape (use scrape)
• When you need comprehensive coverage of a single website (use map or crawl)

Common mistakes:

• Using crawl or map for open-ended questions (use search instead)

Usage Example:

{
  "name": "firecrawl_search",
  "arguments": {
    "query": "latest AI research papers 2023",
    "limit": 5,
    "lang": "en",
    "country": "us",
    "scrapeOptions": {
      "formats": ["markdown"],
      "onlyMainContent": true
    }
  }
}

Returns:

• Array of search results (with optional scraped content)

Prompt Example:

"Find the latest research papers on AI published in 2023."

Starts an asynchronous crawl job on a website and extract content from all pages.

Best for:

• Extracting content from multiple related pages, when you need comprehensive coverage.

Not recommended for:

• Extracting content from a single page (use scrape)
• When token limits are a concern (use map + batch_scrape)
• When you need fast results (crawling can be slow)

Warning: Crawl responses can be very large and may exceed token limits. Limit the crawl depth and number of pages, or use map + batch_scrape for better control.

Common mistakes:

• Setting limit or maxDepth too high (causes token overflow)
• Using crawl for a single page (use scrape instead)

Prompt Example:

"Get all blog posts from the first two levels of example.com/blog."

Usage Example:

{
  "name": "firecrawl_crawl",
  "arguments": {
    "url": "https://example.com/blog/*",
    "maxDepth": 2,
    "limit": 100,
    "allowExternalLinks": false,
    "deduplicateSimilarURLs": true
  }
}

Returns:

• Response includes operation ID for status checking:

{
  "content": [
    {
      "type": "text",
      "text": "Started crawl for: https://example.com/* with job ID: 550e8400-e29b-41d4-a716-446655440000. Use firecrawl_check_crawl_status to check progress."
    }
  ],
  "isError": false
}

Check the status of a crawl job.

{
  "name": "firecrawl_check_crawl_status",
  "arguments": {
    "id": "550e8400-e29b-41d4-a716-446655440000"
  }
}

Returns:

• Response includes the status of the crawl job:

Extract structured information from web pages using LLM capabilities. Supports both cloud AI and self-hosted LLM extraction.

Best for:

• Extracting specific structured data like prices, names, details.

Not recommended for:

• When you need the full content of a page (use scrape)
• When you're not looking for specific structured data

Arguments:

• urls: Array of URLs to extract information from
• prompt: Custom prompt for the LLM extraction
• systemPrompt: System prompt to guide the LLM
• schema: JSON schema for structured data extraction
• allowExternalLinks: Allow extraction from external links
• enableWebSearch: Enable web search for additional context
• includeSubdomains: Include subdomains in extraction

When using a self-hosted instance, the extraction will use your configured LLM. For cloud API, it uses Firecrawl's managed LLM service. Prompt Example:

"Extract the product name, price, and description from these product pages."

Usage Example:

{
  "name": "firecrawl_extract",
  "arguments": {
    "urls": ["https://example.com/page1", "https://example.com/page2"],
    "prompt": "Extract product information including name, price, and description",
    "systemPrompt": "You are a helpful assistant that extracts product information",
    "schema": {
      "type": "object",
      "properties": {
        "name": { "type": "string" },
        "price": { "type": "number" },
        "description": { "type": "string" }
      },
      "required": ["name", "price"]
    },
    "allowExternalLinks": false,
    "enableWebSearch": false,
    "includeSubdomains": false
  }
}

Returns:

• Extracted structured data as defined by your schema

{
  "content": [
    {
      "type": "text",
      "text": {
        "name": "Example Product",
        "price": 99.99,
        "description": "This is an example product description"
      }
    }
  ],
  "isError": false
}

Conduct deep web research on a query using intelligent crawling, search, and LLM analysis.

Best for:

• Complex research questions requiring multiple sources, in-depth analysis.

Not recommended for:

• Simple questions that can be answered with a single search
• When you need very specific information from a known page (use scrape)
• When you need results quickly (deep research can take time)

Arguments:

• query (string, required): The research question or topic to explore.
• maxDepth (number, optional): Maximum recursive depth for crawling/search (default: 3).
• timeLimit (number, optional): Time limit in seconds for the research session (default: 120).
• maxUrls (number, optional): Maximum number of URLs to analyze (default: 50).

Prompt Example:

"Research the environmental impact of electric vehicles versus gasoline vehicles."

Usage Example:

{
  "name": "firecrawl_deep_research",
  "arguments": {
    "query": "What are the environmental impacts of electric vehicles compared to gasoline vehicles?",
    "maxDepth": 3,
    "timeLimit": 120,
    "maxUrls": 50
  }
}

Returns:

• Final analysis generated by an LLM based on research. (data.finalAnalysis)
• May also include structured activities and sources used in the research process.

Generate a standardized llms.txt (and optionally llms-full.txt) file for a given domain. This file defines how large language models should interact with the site.

Best for:

• Creating machine-readable permission guidelines for AI models.

Not recommended for:

• General content extraction or research

Arguments:

• url (string, required): The base URL of the website to analyze.
• maxUrls (number, optional): Max number of URLs to include (default: 10).
• showFullText (boolean, optional): Whether to include llms-full.txt contents in the response.

Prompt Example:

"Generate an LLMs.txt file for example.com."

Usage Example:

{
  "name": "firecrawl_generate_llmstxt",
  "arguments": {
    "url": "https://example.com",
    "maxUrls": 20,
    "showFullText": true
  }
}

Returns:

• LLMs.txt file contents (and optionally llms-full.txt)

The server includes comprehensive logging:

• Operation status and progress
• Performance metrics
• Credit usage monitoring
• Rate limit tracking
• Error conditions

Example log messages:

[INFO] Firecrawl MCP Server initialized successfully
[INFO] Starting scrape for URL: https://example.com
[INFO] Batch operation queued with ID: batch_1
[WARNING] Credit usage has reached warning threshold
[ERROR] Rate limit exceeded, retrying in 2s...

The server provides robust error handling:

• Automatic retries for transient errors
• Rate limit handling with backoff
• Detailed error messages
• Credit usage warnings
• Network resilience

Example error response:

{
  "content": [
    {
      "type": "text",
      "text": "Error: Rate limit exceeded. Retrying in 2 seconds..."
    }
  ],
  "isError": true
}

# Install dependencies
npm install

# Build
npm run build

# Run tests
npm test

1. Fork the repository
2. Create your feature branch
3. Run tests: npm test
4. Submit a pull request

Thanks to @vrknetha, @cawstudios for the initial implementation!

Thanks to MCP.so and Klavis AI for hosting and @gstarwd, @xiangkaiz and @zihaolin96 for integrating our server.

MIT License - see LICENSE file for details

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