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invoke_firecrawl_llmtxt

Crawl websites to generate structured LLM-ready markdown files using Firecrawl, extracting data for AI inference and storing results in S3.

Instructions

Start an asynchronous llmfull.txt generation job using Firecrawl. This file is a standardized markdown file containing information to help LLMs use a website at inference time. The llmstxt endpoint leverages Firecrawl to crawl your website and extracts data using gpt-4o-mini Args: url: URL to crawl s3_uri: S3 URI where results will be uploaded max_urls: Maximum number of pages to crawl (1-100, default: 10)

Returns:
    Dictionary with job information including the job ID

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>",
      "s3_uri": "<s3_uri>"
    },
    "name": "invoke_firecrawl_llmtxt"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes
`s3_uri`	Yes
`max_urls`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv1.0.0Jan 2, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "result": {
+      "additionalProperties": true,
+      "title": "Result",
+      "type": "object"
+    }
+  },
+  "required": [
+    "result"
+  ],
+  "title": "invoke_firecrawl_llmtxtOutput",
+  "type": "object"
+}

First observedMar 24, 2025

TDQS

A3.7/5.0

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 discloses key behavioral traits: asynchronous operation, Firecrawl crawling, GPT-4o-mini extraction, and S3 upload destination. However, it doesn't mention rate limits, authentication requirements, error handling, or job monitoring aspects that would be important for an asynchronous tool.

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

Conciseness5/5

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

The description is well-structured and appropriately sized. It starts with the core purpose, explains what the tool produces, then lists parameters and return value in clear sections. Every sentence earns its place with no redundant information.

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 complexity (asynchronous job with crawling and AI extraction), no annotations, and the presence of an output schema, the description provides good coverage. It explains the purpose, parameters, and return value, though could benefit from more behavioral context about job monitoring (hinted at by sibling tools like check_llmtxt_status) and error scenarios.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must compensate, which it does effectively. It provides clear semantics for all 3 parameters: URL to crawl, S3 URI for results, and max_urls with range and default. The description adds meaningful context beyond what the bare schema provides, though it could elaborate on URL format requirements or S3 URI structure.

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: 'Start an asynchronous llmfull.txt generation job using Firecrawl' with specific details about what the file contains and how it's generated. It distinguishes from siblings like 'invoke_firecrawl_crawlhtml' by specifying the 'llmfull.txt' output format and GPT-4o-mini extraction, though it doesn't explicitly contrast with all siblings.

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

Usage Guidelines3/5

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

The description implies usage for generating standardized markdown files from websites, but doesn't explicitly state when to use this tool versus alternatives like 'invoke_firecrawl_crawlhtml' or other job-related tools. It provides context about the output format but lacks explicit guidance on tool selection scenarios.

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