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0xzapata

Firecrawl MCP Server

firecrawl_extract

Extract structured information from web pages by providing URLs, a prompt, and an optional JSON schema for AI-powered data extraction.

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. 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

  • systemPrompt: System prompt to guide the LLM

  • 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",
    "systemPrompt": "You are a helpful assistant that extracts product information",
    "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
urlsYesList of URLs to extract information from
promptNoPrompt for the LLM extraction
systemPromptNoSystem prompt for LLM extraction
schemaNoJSON schema for structured data extraction
allowExternalLinksNoAllow extraction from external links
enableWebSearchNoEnable web search for additional context
includeSubdomainsNoInclude subdomains in extraction
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 support for both cloud AI and self-hosted LLM extraction, and states the return type ('Extracted structured data as defined by your schema'). However, it does not disclose important behavioral traits such as rate limits, authentication requirements, or whether the tool modifies any data (though it appears read-only). The description is adequate but not comprehensive in this dimension.

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/not recommended, arguments list, example, and returns. It is front-loaded with the main purpose and every sentence adds value. There is no redundant or irrelevant information.

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

Completeness5/5

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

Given the parameter count of 7 (all documented), no output schema, and nested objects, the description covers purpose, usage guidelines, all parameters with examples, and return type. It references sibling tools for differentiation. It is complete and self-contained for an AI agent to understand and invoke the tool correctly.

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?

Schema description coverage is 100%, so the baseline is 3. However, the description goes beyond by providing a prompt example and a full usage example that demonstrates how to use the parameters together, especially the 'schema' as a nested object. This added context elevates the score to 4.

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 that the tool extracts structured information from web pages using LLM capabilities. It provides a specific verb ('Extract') and resource ('web pages'), and distinguishes itself from sibling tools by noting that it's best for structured data and not recommended for full content (which points to scrape). This meets the highest standard for purpose clarity.

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 includes 'Best for' and 'Not recommended for' sections, giving clear when-to-use and when-not-to-use guidance. It directly references the sibling tool 'scrape' as an alternative for full page content. This provides excellent usage guidelines.

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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