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Firecrawl MCP Server

firecrawl_generate_llmstxt

Generate machine-readable permission guidelines (LLMs.txt files) for websites to define how AI models should interact with their content.

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

  • Handler logic for the firecrawl_generate_llmstxt tool.
    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,
        };
      }
    }
  • Schema definition 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:972-972 (registration)
    Registration of the firecrawl_generate_llmstxt tool in the server request handler.
    ],

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.12.0

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description carries full burden. It mentions the tool generates and returns file contents, but does not disclose side effects, permissions, rate limits, or whether it is read-only. The behavioral traits are implied but not explicit.

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 relatively long but well-structured with clear sections (Best for, Arguments, Examples). It includes a usage example which is helpful. However, some redundancy exists (e.g., arguments listed twice). Overall efficient for the information provided.

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?

With no output schema, the description minimally explains return values ('LLMs.txt file contents'). It covers all parameters and provides usage examples. It could be more precise about the response format or data type, but is sufficient for understanding.

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 coverage is 100%, so baseline is 3. The description repeats parameter names and adds minor context (e.g., 'base URL'), but does not add substantial meaning beyond the schema. For maxUrls, it omits the range provided in 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 generates a standardized llms.txt file, with a specific verb and resource. It distinguishes from siblings by explicitly stating 'Best for' and 'Not recommended for' contexts, differentiating from general extraction or research tools.

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 usage context with 'Best for' and 'Not recommended for' sections, guiding when to use this tool over alternatives. It does not name specific sibling tools but the contrast is clear.

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