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AdvaitR7

Firecrawl MCP Multiple Keys

by AdvaitR7

firecrawl_extract

Read-only

Extract structured data from web pages by defining a custom prompt or JSON schema. Get specific fields like product names and prices from one or more URLs.

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
enableWebSearchNo
includeSubdomainsNo
allowExternalLinksNo
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety profile is covered. Description adds LLM method support (cloud/self-hosted) but no additional behavioral traits like rate limits, error handling, or authorization needs.

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?

Well-structured with clear sections: purpose, best/not recommended, argument list, prompt example, usage example, return description. No fluff; every sentence adds value.

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?

Covers purpose, usage, all parameters with examples. Lacks details on error handling or authentication, but given annotations cover safety and output depends on user-provided schema, it is reasonably complete.

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 0%, so description must compensate. It provides argument descriptions, a prompt example, and a full usage example with schema. While terse, it adds meaning beyond bare parameter names.

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?

Clearly states the verb (Extract), resource (structured information from web pages), and method (using LLM capabilities). Explicitly distinguishes from sibling tools like scrape and crawl with 'Not recommended for' sections.

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?

Explicitly states when to use ('Best for: extracting specific structured data') and when not to use ('Not recommended for: full content, use scrape'). Provides clear alternatives and context.

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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