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by dev.cloro

Scrape Google AI Mode

scrape_google_ai_mode
Read-only

Submit a prompt to Google AI Mode from a chosen country and return the AI answer with cited sources. Supports location or UULE targeting and desktop/mobile emulation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
glNoISO 3166-1 alpha-2 code for the result geography, sent to Google as gl (e.g. "us"). Use list_countries to see supported codes per model.
hlNoOptional Google interface-language code, sent as hl (e.g. "de", "pt-br"). Defaults to the language derived from gl — set it when the geography's dominant language is not the one you want.
uuleNoOptional Google UULE location parameter. Mutually exclusive with location.
deviceNoDevice type to emulate. Defaults to desktop.desktop
promptYesThe prompt to submit to Google AI Mode.
countryNoISO 3166-1 alpha-2 country code to geo-target the request from (e.g. "US"). Use list_countries to see supported codes per model. Deprecated — use gl instead; country is kept for compatibility and must not disagree with gl.
includeNoOptional flags to include heavier payload fields in the response. Leave unset for the leanest response.
locationNoOptional location name to target search results (e.g. "Austin, Texas, United States"). Mutually exclusive with uule.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already cover read-only/open-world/non-destructive behavior. The description adds useful behavioral context by stating that the tool returns an AI answer with cited sources and supports location/UULE targeting and desktop/mobile emulation, which goes beyond the annotation fields.

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?

Two focused sentences with front-loaded purpose and minimal waste. The first sentence establishes what the tool does and returns; the second summarizes key targeting/emulation capabilities.

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?

The detailed schema plus annotations cover most operational details, and the description explains the core return value ('AI answer with cited sources'). Minor contextual gaps remain around the optional include payload options, but the schema partially covers those and no output schema exists.

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 baseline is 3. The description's mention of 'location or UULE targeting' and 'desktop/mobile emulation' restates what the schema already documents and does not add deeper semantic guidance beyond the parameters.

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 names a specific verb+resource: 'Submit a prompt to Google AI Mode' and a concrete expected output: 'return the AI answer with cited sources.' This distinguishes it from sibling scraper tools by naming the exact Google product and output type.

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 description gives clear context for when to use this tool: when an agent needs a Google AI Mode response, optionally geo-targeted by country/location/UULE or with device emulation. It does not explicitly name alternatives or exclusion cases, but the scenario is clear enough among the sibling scraper tools.

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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TDQS

A4/5.0
Disambiguation5/5

Each tool targets a distinct engine or product (ChatGPT, Copilot, Gemini, Google, Google News, etc.), and the descriptions clearly specify what each scrape returns. The only potential overlap is between Google search and Google AI Mode, but the descriptions separate them effectively.

Naming Consistency5/5

All tools follow a consistent snake_case verb_noun pattern: list_* for metadata and scrape_* for retrieval operations. The engine-specific names like scrape_chatgpt and scrape_google_ai_mode are predictable and easy to group.

Tool Count5/5

Ten tools is a well-scoped size for a geo-targeted search and AI answer scraping server. Each tool covers a meaningful engine or metadata requirement without excessive redundancy.

Completeness4/5

The tool surface covers major AI assistants, Google search variants, news, and supporting geo-targeting metadata. Minor gaps exist such as no standalone Bing/DuckDuckGo scraper or explicit engine model listing, but the core workflows are well supported.

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