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NYO2008

Firecrawl MCP Server

by NYO2008

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 primary handler logic for the 'firecrawl_generate_llmstxt' tool. It performs input validation, invokes the Firecrawl client's generateLLMsText method with retry logic, processes the response (including optional llms-full.txt), and formats the output or handles errors.
    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,
        };
      }
    }
  • Tool schema definition including name, detailed description, and inputSchema for parameter validation (url required, maxUrls and showFullText optional).
    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:955-966 (registration)
    Registration of the tool in the list returned by ListToolsRequestSchema handler, making it discoverable by MCP clients.
    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 in the handler to validate input arguments for the 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'
      );
    }
  • TypeScript interface defining optional parameters for the LLMs.txt generation (maxUrls, showFullText, experimental_stream). Used in type guard and handler.
    interface GenerateLLMsTextParams {
      /**
       * Maximum number of URLs to process (1-100)
       * @default 10
       */
      maxUrls?: number;
      /**
       * Whether to show the full LLMs-full.txt in the response
       * @default false
       */
      showFullText?: boolean;
      /**
       * Experimental flag for streaming
       */
      __experimental_stream?: boolean;
    }
Behavior3/5

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

With no annotations provided, the description carries full burden. It describes what the tool does (generates files) and what it returns, but lacks details on behavioral traits like rate limits, authentication needs, error conditions, or whether it performs web crawling/analysis. The description doesn't contradict annotations (none exist), but provides only basic 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 for, Arguments, Prompt Example, Usage Example, Returns). It's appropriately sized for a 3-parameter tool, though the usage example could be more concise. Most sentences earn their place by providing distinct information.

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

Completeness3/5

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

Given no annotations and no output schema, the description provides adequate context about what the tool does and when to use it, but lacks details about behavioral characteristics, error handling, or what the return format looks like beyond 'LLMs.txt file contents.' For a tool that presumably crawls websites and generates files, more operational context would be helpful.

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 baseline is 3. The description adds minimal value beyond the schema: it repeats parameter names and provides a usage example, but doesn't explain parameter interactions or provide additional semantic context. The 'Arguments:' section essentially restates what's in 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: 'Generate a standardized llms.txt (and optionally llms-full.txt) file for a given domain.' It specifies both the verb ('generate') and resource ('llms.txt file'), and distinguishes from siblings by noting this is for 'machine-readable permission guidelines for AI models' rather than general extraction or research.

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, clearly stating when to use this tool versus alternatives. It distinguishes this from 'General content extraction or research,' which helps differentiate from sibling tools like firecrawl_extract or firecrawl_deep_research.

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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