Firecrawl MCP Server

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: Multiple pages (use batch_scrape), unknown page (use search), 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"]
  }
}

Returns: Markdown, HTML, or other formats as specified.

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.

Starts an asynchronous crawl job on a website and extracts 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: Operation ID for status checking; use firecrawl_check_crawl_status to check progress.

Check the status of a crawl job.

Usage Example:

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

Returns: Status and progress of the crawl job, including results if available.

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). Prompt Example: "Find the latest research papers on AI published in 2023." 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).

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 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.

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).