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google_search_scraper

Read-only

Scrape Google search results with support for parsing, pagination, geolocation, and locale settings to extract structured data efficiently.

Instructions

Scrape Google Search results.

Supports content parsing, different user agent types, pagination, domain, geolocation, locale parameters and different output formats.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Number of results to retrieve in each page.
`pages`	No	Number of pages to retrieve.
`parse`	No	Should result be parsed. If the result is not parsed, the output_format parameter is applied.
`query`	Yes	URL-encoded keyword to search for.
`domain`	No	Domain localization for Google. Use country top level domains. For example: - 'co.uk' for United Kingdom - 'us' for United States - 'fr' for France
`locale`	No	Set 'Accept-Language' header value which changes your Google search page web interface language. Examples: - 'en-US' for English, United States - 'de-AT' for German, Austria - 'fr-FR' for French, France
`render`	No	Whether a headless browser should be used to render the page. For example: - 'html' when browser is required to render the page.
`ad_mode`	No	If true will use the Google Ads source optimized for the paid ads.
`start_page`	No	Starting page number.
`geo_location`	No	The geographical location that the result should be adapted for. Use ISO-3166 country codes. Examples: - 'California, United States' - 'Mexico' - 'US' for United States - 'DE' for Germany - 'FR' for France
`output_format`	No	The format of the output. Works only when parse parameter is false. - links - Most efficient when the goal is navigation or finding specific URLs. Use this first when you need to locate a specific page within a website. - md - Best for extracting and reading visible content once you've found the right page. Use this to get structured content that's easy to read and process. - html - Should be used sparingly only when you need the raw HTML structure, JavaScript code, or styling information.
`user_agent_type`	No	Device type and browser that will be used to determine User-Agent header value.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.8.1May 6, 2026

TDQS

A3.7/5.0

Behavior3/5

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

Annotations declare readOnlyHint=true, and the description 'Scrape' is consistent with a read operation. However, the description adds little beyond the annotations and the parameter schema; it does not mention rate limits, pagination behavior, rendering implications, or antiscraping nuances.

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 only two sentences. The first sentence is a clear, front-loaded purpose statement; the second is a compact capability list. There is no filler or redundancy.

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?

The schema and output schema are rich, but the tool description omits several notable parameters like render and ad_mode, and does not explain the parse/output_format relationship. Given the tool's complexity, the description alone provides only high-level context, leaving these gaps.

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 description coverage is 100%, so the schema fully documents all 12 parameters. The tool description only lists categories like 'pagination' and 'geolocation' without adding new meaning; therefore, a baseline 3 is appropriate.

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 starts with a specific verb and resource: 'Scrape Google Search results.' This clearly distinguishes the tool from siblings like amazon_search_scraper or ai_search by naming Google Search as the target. The second sentence enumerates key capabilities (parsing, user agents, pagination, etc.), further clarifying scope.

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 scraping Google Search results but provides no explicit guidance on when to prefer this over ai_search, universal_scraper, or other siblings. It lists supported features but does not state conditions, exclusions, or alternative choices.

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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