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Firecrawl MCP Server

by ampcome-mcps

firecrawl_generate_llmstxt

Generate machine-readable permission guidelines for AI models by creating standardized llms.txt files that define how large language models should interact with websites.

Instructions

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).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe URL to generate LLMs.txt from
maxUrlsNoMaximum number of URLs to process (1-100, default: 10)
showFullTextNoWhether to show the full LLMs-full.txt in the response

Implementation Reference

  • The handler for the 'firecrawl_generate_llmstxt' tool. Validates input using isGenerateLLMsTextOptions, calls client.generateLLMsText with retry logic, formats the LLMs.txt and optional llms-full.txt content, and returns the result or error.
    case 'firecrawl_generate_llmstxt': {
      if (!isGenerateLLMsTextOptions(args)) {
        throw new Error('Invalid arguments for firecrawl_generate_llmstxt');
      }
    
      try {
        const { url, ...params } = args;
        const generateStartTime = Date.now();
    
        safeLog('info', `Starting LLMs.txt generation for URL: ${url}`);
    
        // Start the generation process
        const response = await withRetry(
          async () =>
            // @ts-expect-error Extended API options including origin
            client.generateLLMsText(url, { ...params, origin: 'mcp-server' }),
          'LLMs.txt generation'
        );
    
        if (!response.success) {
          throw new Error(response.error || 'LLMs.txt generation failed');
        }
    
        // Log performance metrics
        safeLog(
          'info',
          `LLMs.txt generation completed in ${Date.now() - generateStartTime}ms`
        );
    
        // Format the response
        let resultText = '';
    
        if ('data' in response) {
          resultText = `LLMs.txt content:\n\n${response.data.llmstxt}`;
    
          if (args.showFullText && response.data.llmsfulltxt) {
            resultText += `\n\nLLMs-full.txt content:\n\n${response.data.llmsfulltxt}`;
          }
        }
    
        return {
          content: [{ type: 'text', text: trimResponseText(resultText) }],
          isError: false,
        };
      } catch (error) {
        const errorMessage =
          error instanceof Error ? error.message : String(error);
        return {
          content: [{ type: 'text', text: trimResponseText(errorMessage) }],
          isError: true,
        };
      }
    }
  • The Tool object defining the schema, name, description, and inputSchema for the firecrawl_generate_llmstxt tool.
    const GENERATE_LLMSTXT_TOOL: Tool = {
      name: 'firecrawl_generate_llmstxt',
      description: `
    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:**
    \`\`\`json
    {
      "name": "firecrawl_generate_llmstxt",
      "arguments": {
        "url": "https://example.com",
        "maxUrls": 20,
        "showFullText": true
      }
    }
    \`\`\`
    **Returns:** LLMs.txt file contents (and optionally llms-full.txt).
    `,
      inputSchema: {
        type: 'object',
        properties: {
          url: {
            type: 'string',
            description: 'The URL to generate LLMs.txt from',
          },
          maxUrls: {
            type: 'number',
            description: 'Maximum number of URLs to process (1-100, default: 10)',
          },
          showFullText: {
            type: 'boolean',
            description: 'Whether to show the full LLMs-full.txt in the response',
          },
        },
        required: ['url'],
      },
    };
  • src/index.ts:962-973 (registration)
    Registration of the firecrawl_generate_llmstxt 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 used to validate the input arguments for the firecrawl_generate_llmstxt tool.
    function isGenerateLLMsTextOptions(
      args: unknown
    ): args is { url: string } & Partial<GenerateLLMsTextParams> {
      return (
        typeof args === 'object' &&
        args !== null &&
        'url' in args &&
        typeof (args as { url: unknown }).url === 'string'
      );
    }
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions what the tool generates (llms.txt files) and includes a usage example, but lacks details on behavioral traits like rate limits, authentication needs, error handling, or how it analyzes the domain. The description adds some context but is incomplete for a tool with no annotation coverage.

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 sections like 'Best for:', 'Arguments:', and usage examples, making it easy to scan. It's appropriately sized but includes a prompt example that could be considered slightly redundant, though it adds practical value. Most sentences earn their place.

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?

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is fairly complete. It covers purpose, usage guidelines, parameters, and return values ('Returns: LLMs.txt file contents'), but lacks details on behavioral aspects like processing time or error cases, which would be helpful for full completeness.

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 arguments with brief notes (e.g., 'default: 10' for maxUrls), but adds minimal semantic value beyond what's in the schema. This meets the baseline of 3 when schema coverage is high.

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: 'Generate a standardized llms.txt (and optionally llms-full.txt) file for a given domain.' It specifies the verb ('generate'), resource ('llms.txt file'), and scope ('for a given domain'), distinguishing it from sibling tools like 'extract' or 'scrape' that handle general 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 explicitly provides usage guidance with 'Best for:' (creating machine-readable permission guidelines for AI models) and 'Not recommended for:' (general content extraction or research), clearly differentiating when to use this tool versus alternatives like 'firecrawl_extract' or 'firecrawl_scrape'.

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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