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NYO2008

Firecrawl MCP Server

by NYO2008

firecrawl_extract

Extract structured data from web pages using AI, converting unstructured content into organized information like prices or product details.

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. 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 logic for executing the firecrawl_extract tool. It validates the arguments, calls the Firecrawl client's extract method with retry logic, formats the response, and handles errors including self-hosted limitations.
    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);
        }
    
        return result;
      } catch (error) {
        const errorMessage =
          error instanceof Error ? error.message : String(error);
    
        // Special handling for self-hosted instance errors
        if (
          FIRECRAWL_API_URL &&
          errorMessage.toLowerCase().includes('not supported')
        ) {
          safeLog(
            'error',
            'Extraction is not supported by this self-hosted instance'
          );
          return {
            content: [
              {
                type: 'text',
                text: trimResponseText(
                  'Extraction is not supported by this self-hosted instance. Please ensure LLM support is configured.'
                ),
              },
            ],
            isError: true,
          };
        }
    
        return {
          content: [{ type: 'text', text: trimResponseText(errorMessage) }],
          isError: true,
        };
      }
    }
  • Tool definition for firecrawl_extract, including name, detailed description, and inputSchema specifying parameters like urls (required), prompt, systemPrompt, schema, etc.
    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.
    **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": "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.
    `,
      inputSchema: {
        type: 'object',
        properties: {
          urls: {
            type: 'array',
            items: { type: 'string' },
            description: 'List of URLs to extract information from',
          },
          prompt: {
            type: 'string',
            description: 'Prompt for the LLM extraction',
          },
          systemPrompt: {
            type: 'string',
            description: 'System prompt for LLM extraction',
          },
          schema: {
            type: 'object',
            description: 'JSON schema for structured data extraction',
          },
          allowExternalLinks: {
            type: 'boolean',
            description: 'Allow extraction from external links',
          },
          enableWebSearch: {
            type: 'boolean',
            description: 'Enable web search for additional context',
          },
          includeSubdomains: {
            type: 'boolean',
            description: 'Include subdomains in extraction',
          },
        },
        required: ['urls'],
      },
    };
  • src/index.ts:955-966 (registration)
    Registration of the firecrawl_extract tool (as EXTRACT_TOOL) in the list of available tools returned by ListToolsRequestSchema handler.
    server.setRequestHandler(ListToolsRequestSchema, async () => ({
      tools: [
        SCRAPE_TOOL,
        MAP_TOOL,
        CRAWL_TOOL,
        CHECK_CRAWL_STATUS_TOOL,
        SEARCH_TOOL,
        EXTRACT_TOOL,
        DEEP_RESEARCH_TOOL,
        GENERATE_LLMSTXT_TOOL,
      ],
    }));
  • Type guard helper function to validate that arguments for firecrawl_extract contain a valid array of string URLs.
    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 the full burden. It explains the core behavior (LLM-based extraction) and mentions support for cloud/self-hosted LLMs, but lacks details on rate limits, authentication requirements, error handling, or what happens with invalid URLs/schemas. The description doesn't contradict annotations (none exist), but could provide more operational context.

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

Conciseness4/5

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

The description is well-structured with clear sections (Best for, Not recommended, Arguments, Examples). It's appropriately sized for a 7-parameter tool. However, the prompt and usage examples are quite detailed, making it slightly verbose. Every section serves a purpose, but some redundancy exists between the argument list and schema descriptions.

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 extraction tool with 7 parameters (including nested schema) and no output schema, the description does well. It explains the tool's purpose, when to use it, provides parameter guidance, and shows complete examples. The main gap is lack of output format details beyond 'Returns: Extracted structured data as defined by your schema' - more specificity about the return structure would help.

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. It provides a usage example showing how parameters work together, which adds some context, but doesn't explain trade-offs or relationships between parameters like 'allowExternalLinks' and 'enableWebSearch'.

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 method (LLM capabilities). It also distinguishes from sibling tools by explicitly stating when not to use it (vs. 'scrape' for full content).

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 alternatives ('use scrape'). It clearly defines the appropriate context (extracting specific structured data) and when to avoid it (when needing full content or not looking for structured data).

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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