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Firecrawl MCP Server

by mendableai

firecrawl_extract

Extract structured data from web pages using LLM capabilities. Define specific information to retrieve with custom prompts and JSON schemas for organized output.

Instructions

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

Best for: Extracting specific structured data like prices, names, details from web pages. Not recommended for: When you need the full content of a page (use scrape); when you're not looking for specific structured data. Arguments:

  • urls: Array of URLs to extract information from

  • prompt: Custom prompt for the LLM extraction

  • schema: JSON schema for structured data extraction

  • allowExternalLinks: Allow extraction from external links

  • enableWebSearch: Enable web search for additional context

  • includeSubdomains: Include subdomains in extraction Prompt Example: "Extract the product name, price, and description from these product pages." Usage Example:

{
  "name": "firecrawl_extract",
  "arguments": {
    "urls": ["https://example.com/page1", "https://example.com/page2"],
    "prompt": "Extract product information including name, price, and description",
    "schema": {
      "type": "object",
      "properties": {
        "name": { "type": "string" },
        "price": { "type": "number" },
        "description": { "type": "string" }
      },
      "required": ["name", "price"]
    },
    "allowExternalLinks": false,
    "enableWebSearch": false,
    "includeSubdomains": false
  }
}

Returns: Extracted structured data as defined by your schema.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYes
promptNo
schemaNo
allowExternalLinksNo
enableWebSearchNo
includeSubdomainsNo
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior as an LLM-powered extraction service that returns structured data according to a schema. However, it doesn't mention important behavioral aspects like rate limits, authentication requirements, error handling, or whether the extraction is deterministic versus probabilistic.

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 with clear sections (purpose, best for/not for, arguments, examples) and every sentence earns its place. The information is front-loaded with the core purpose first, followed by practical guidance, then detailed parameter explanations with concrete examples.

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 (6 parameters with nested objects, no output schema, no annotations), the description does an excellent job explaining parameters and usage. However, it lacks information about the return format beyond 'Extracted structured data as defined by your schema' - without an output schema, more detail about response structure would be helpful for a complex extraction tool.

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

Parameters5/5

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

With 0% schema description coverage for 6 parameters, the description fully compensates by providing clear explanations for all parameters in the 'Arguments:' section, plus a detailed usage example showing how each parameter should be structured. It adds substantial meaning beyond what the bare schema provides, explaining what each parameter controls in the extraction process.

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 as 'Extract structured information from web pages using LLM capabilities' with specific verbs ('extract') and resources ('structured information from web pages'). It distinguishes from sibling tools by explicitly contrasting with 'scrape' for full content extraction and positioning this for specific structured data 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 provides explicit guidance with 'Best for:' and 'Not recommended for:' sections, clearly stating when to use this tool (extracting specific structured data) versus alternatives (use 'scrape' for full content). It names the specific sibling tool 'scrape' as the alternative for different use cases.

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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