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mcma123

Firecrawl MCP Server

by mcma123

firecrawl_extract

Extract structured data from web pages using AI, converting URLs into organized information with custom schemas and prompts.

Instructions

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

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

  • Handler for the 'firecrawl_extract' tool. Validates input using isExtractOptions, calls the Firecrawl client's extract method with parameters, handles responses, credits, logging, and errors including special handling for self-hosted instances.
    case 'firecrawl_extract': {
      if (!isExtractOptions(args)) {
        throw new Error('Invalid arguments for firecrawl_extract');
      }
    
      try {
        const extractStartTime = Date.now();
    
        server.sendLoggingMessage({
          level: 'info',
          data: `Starting extraction for URLs: ${args.urls.join(', ')}`,
        });
    
        // Log if using self-hosted instance
        if (FIRECRAWL_API_URL) {
          server.sendLoggingMessage({
            level: 'info',
            data: '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;
    
        // Monitor credits for cloud API
        if (!FIRECRAWL_API_URL && hasCredits(response)) {
          await updateCreditUsage(response.creditsUsed || 0);
        }
    
        // Log performance metrics
        server.sendLoggingMessage({
          level: 'info',
          data: `Extraction completed in ${Date.now() - extractStartTime}ms`,
        });
    
        // Add warning to response if present
        const result = {
          content: [
            {
              type: 'text',
              text: JSON.stringify(response.data, null, 2),
            },
          ],
          isError: false,
        };
    
        if (response.warning) {
          server.sendLoggingMessage({
            level: 'warning',
            data: 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')
        ) {
          server.sendLoggingMessage({
            level: 'error',
            data: 'Extraction is not supported by this self-hosted instance',
          });
          return {
            content: [
              {
                type: 'text',
                text: 'Extraction is not supported by this self-hosted instance. Please ensure LLM support is configured.',
              },
            ],
            isError: true,
          };
        }
    
        return {
          content: [{ type: 'text', text: errorMessage }],
          isError: true,
        };
      }
    }
  • Tool schema definition for 'firecrawl_extract' including name, description, and detailed inputSchema with properties for URLs, prompts, schema, and extraction options.
    const EXTRACT_TOOL: Tool = {
      name: 'firecrawl_extract',
      description:
        'Extract structured information from web pages using LLM. ' +
        'Supports both cloud AI and self-hosted LLM extraction.',
      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:862-874 (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,
        BATCH_SCRAPE_TOOL,
        CHECK_BATCH_STATUS_TOOL,
        CHECK_CRAWL_STATUS_TOOL,
        SEARCH_TOOL,
        EXTRACT_TOOL,
        DEEP_RESEARCH_TOOL,
      ],
    }));
  • Type guard helper function used to validate arguments for the firecrawl_extract tool.
    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')
      );
    }
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions LLM-based extraction and deployment options but lacks critical details: what permissions or authentication are needed, rate limits, whether it's read-only or modifies data, error handling, or output format. For a tool with 7 parameters and no annotations, this is a significant gap in transparency.

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 concise (two sentences) and front-loaded with the core purpose. Every sentence adds value: the first defines the tool's function, and the second clarifies deployment options. No wasted words, though it could be more structured with bullet points for clarity.

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

Completeness2/5

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

Given the tool's complexity (7 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain the extraction process, output format, error cases, or how it differs from siblings. For an LLM-based extraction tool with multiple parameters, more context is needed to guide effective use.

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?

The input schema has 100% description coverage, so parameters like 'urls,' 'prompt,' and 'schema' are well-documented in the schema itself. The description adds minimal value beyond this, only implying LLM usage and deployment modes without detailing parameter interactions or constraints. Baseline 3 is appropriate given high schema coverage.

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

Purpose4/5

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.' It specifies the verb ('extract'), resource ('structured information from web pages'), and method ('using LLM'). However, it doesn't explicitly differentiate from sibling tools like firecrawl_scrape or firecrawl_deep_research, which likely have overlapping functionality.

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

Usage Guidelines2/5

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

The description provides minimal usage guidance. It mentions support for 'cloud AI and self-hosted LLM extraction,' which hints at deployment options but doesn't specify when to use this tool versus alternatives like firecrawl_scrape (for raw content) or firecrawl_deep_research (for more complex analysis). No explicit when/when-not scenarios or prerequisites are provided.

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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