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ashishdevthakur3-max

Firecrawl MCP Server

firecrawl_extract

Extract structured data like product details or prices from web pages using LLM capabilities with custom prompts and JSON schemas.

Instructions

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 from web pages. 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesList of URLs to extract information from
promptNoPrompt for the LLM extraction
systemPromptNoSystem prompt for LLM extraction
schemaNoJSON schema for structured data extraction
allowExternalLinksNoAllow extraction from external links
enableWebSearchNoEnable web search for additional context
includeSubdomainsNoInclude subdomains in extraction

Implementation Reference

  • The handler implementation for firecrawl_extract, processing the request and calling the client.extract method.
    case 'firecrawl_extract': {
      if (!isExtractOptions(args)) {
        throw new Error('Invalid arguments for firecrawl_extract');
      }
    
      try {
        const extractStartTime = Date.now();
    
        safeLog(
          'info',
          `Starting extraction for URLs: ${args.urls.join(', ')}`
        );
    
        // Log if using self-hosted instance
        if (FIRECRAWL_API_URL) {
          safeLog('info', 'Using self-hosted instance for extraction');
        }
    
        const extractResponse = await withRetry(
          async () =>
            client.extract(args.urls, {
              prompt: args.prompt,
              systemPrompt: args.systemPrompt,
              schema: args.schema,
              allowExternalLinks: args.allowExternalLinks,
              enableWebSearch: args.enableWebSearch,
              includeSubdomains: args.includeSubdomains,
              origin: 'mcp-server',
            } as ExtractParams),
          'extract operation'
        );
    
        // Type guard for successful response
        if (!('success' in extractResponse) || !extractResponse.success) {
          throw new Error(extractResponse.error || 'Extraction failed');
        }
    
        const response = extractResponse as ExtractResponse;
    
        // Log performance metrics
        safeLog(
          'info',
          `Extraction completed in ${Date.now() - extractStartTime}ms`
        );
    
        // Add warning to response if present
        const result = {
          content: [
            {
              type: 'text',
              text: trimResponseText(JSON.stringify(response.data, null, 2)),
            },
          ],
          isError: false,
        };
    
        if (response.warning) {
          safeLog('warning', response.warning);
        }
  • The tool definition (schema) for firecrawl_extract.
    const EXTRACT_TOOL: Tool = {
      name: 'firecrawl_extract',
      description: `
    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 from web pages.
    **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:**
    \`\`\`json
    {
      "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 guard helper for validating arguments for firecrawl_extract.
    function isExtractOptions(args: unknown): args is ExtractArgs {
      if (typeof args !== 'object' || args === null) return false;
      const { urls } = args as { urls?: unknown };
      return (
        Array.isArray(urls) &&
        urls.every((url): url is string => typeof url === 'string')
      );
    }
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It explains the core extraction behavior and mentions support for cloud/self-hosted LLMs, but lacks details about rate limits, authentication requirements, error handling, or processing characteristics. The description doesn't contradict any annotations (none exist).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, best for/not for, arguments, examples). Every sentence serves a purpose, and the usage example provides concrete implementation guidance without unnecessary verbosity. The formatting with markdown and code blocks enhances readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with 7 parameters, no annotations, and no output schema, the description does well by explaining the core functionality, providing usage guidelines, and showing a comprehensive example. However, it lacks information about return format details (beyond 'structured data as defined by your schema') and doesn't address potential limitations or error cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters. The description lists parameters but adds minimal semantic value beyond what's in the schema descriptions. The prompt example provides helpful context for the 'prompt' parameter, but overall the description doesn't significantly enhance parameter understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Extract structured information from web pages using LLM capabilities.' It specifies the verb ('extract'), resource ('structured information from web pages'), and distinguishes it from sibling tools by explicitly contrasting with 'scrape' for full content extraction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance with 'Best for:' and 'Not recommended for:' sections, naming specific use cases and alternatives. It clearly distinguishes when to use this tool versus the 'scrape' sibling tool, making selection straightforward.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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