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ScrapeOps

ScrapeOps MCP Server

by ScrapeOps

extract_data

Extract structured data from webpages using automatic parsing or AI-powered extraction with customizable schemas. Supports product, job, company, real estate, and search data.

Instructions

Extract structured data from webpages using auto-parsing or LLM-powered extraction.

Best for:

  • Getting product information (prices, names, descriptions)

  • Extracting job listings data

  • Parsing search results

  • Collecting structured company information

  • Real estate listings extraction

Extraction Modes:

  • auto: Domain-specific parsers for common sites (Amazon, Google, etc.)

  • llm: AI-powered extraction with customizable schemas

Data Schemas (for LLM mode):

  • Product: product_page, product_reviews_page, product_search_page, product_seller_page

  • Jobs: job_page, job_advert_page, job_search_page

  • Company: company_page, company_job_page, company_review_page, company_search_page

  • Real Estate: real_estate_page, real_estate_profile_page, real_estate_search_page

  • Search: serp_search_page

Usage Examples:

  1. Auto-extract from known domain:

{
  "name": "extract_data",
  "arguments": {
    "url": "https://www.amazon.com/dp/B09V3KXJPB",
    "mode": "auto"
  }
}
  1. LLM extraction for product page:

{
  "name": "extract_data",
  "arguments": {
    "url": "https://shop.example.com/product/123",
    "mode": "llm",
    "data_schema": "product_page",
    "response_format": "json"
  }
}
  1. Extract job listings with anti-bot bypass:

{
  "name": "extract_data",
  "arguments": {
    "url": "https://careers.example.com/jobs",
    "mode": "llm",
    "data_schema": "job_search_page",
    "bypass_level": "generic_level_2",
    "render_js": true
  }
}
  1. Extract real estate data in markdown:

{
  "name": "extract_data",
  "arguments": {
    "url": "https://realestate.example.com/listing/456",
    "mode": "llm",
    "data_schema": "real_estate_page",
    "response_format": "markdown"
  }
}

Returns: Structured JSON or Markdown data extracted from the page.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
modeYes
waitNo
mobileNo
countryNo
premiumNo
wait_forNo
render_jsNo
data_schemaNo
device_typeNo
residentialNo
bypass_levelNo
keep_headersNo
session_numberNo
response_formatYesjson
follow_redirectsNo
max_request_costNo
optimize_requestNo
Behavior4/5

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

With no annotations, the description carries full burden and discloses extraction modes, data schemas, anti-bypass options, and return format. However, it omits behaviors for many parameters like wait, mobile, and follow_redirects.

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 well-structured with sections and examples, front-loading the purpose. It is appropriately sized but could be slightly trimmed without losing value.

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

Completeness3/5

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

Given 18 parameters and no output schema, the description covers core functionality well but lacks details on many optional parameters and error/rate-limit behaviors. It is complete for basic usage but not exhaustive.

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 0%, so description compensates by explaining key parameters (url, mode, data_schema, bypass_level) via examples. But many parameters (e.g., wait, country, session_number) remain unexplained, making it only partially helpful.

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 extracts structured data from webpages and provides a list of best-use cases. It distinguishes from siblings via usage examples and mentions auto and LLM modes.

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

Usage Guidelines4/5

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

The 'Best for' section and examples provide clear context for when to use this tool. However, it does not explicitly state when not to use it or alternatives like maps_web or return_links.

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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