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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 for heavier payload fields, each off by default: markdown (the answer rendered as markdown), html (the answer page HTML), rawResponse (the engine's unprocessed response payload). Leave unset for the leanest response.
locationNoOptional location name to target search results (e.g. "Austin, Texas, United States"). Mutually exclusive with uule.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / include / description
      Previous value: -"Optional flags to include heavier payload fields in the response. Leave unset for the leanest response."New value: +"Optional flags for heavier payload fields, each off by default: markdown (the answer rendered as markdown), html (the answer page HTML), rawResponse (the engine's unprocessed response payload). Leave unset for the leanest response."
  2. Changed1 schema field changed
    • addedInput schema / properties / include / properties / googleGoto
      Added value: +{
+  "type": "boolean"
+}
  3. Changed4 schema fields changed
    • changedInput schema / properties / country / description
      Previous value: -"ISO 3166-1 alpha-2 country code to geo-target the request from (e.g. \"US\"). Use list_countries to see supported codes per model."New value: +"ISO 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."
    • addedInput schema / properties / gl
      Added value: +{
+  "description": "ISO 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.",
+  "type": "string"
+}
    • addedInput schema / properties / hl
      Added value: +{
+  "description": "Optional 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.",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "prompt",
-  "country"
-]New value: +[
+  "prompt"
+]
  4. First observed

TDQS

A4/5.0
Behavior4/5

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

Beyond the readOnly/openWorld annotations, the description adds meaningful behavioral context: it returns the AI answer with cited sources and supports country/location targeting and desktop/mobile emulation. It does not detail response structure or rate limits, but the core behavior is disclosed.

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 two sentences with the main action front-loaded and no wasted words. It conveys purpose, return value, and key options without restating schema details.

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?

For a tool with 8 parameters and no output schema, the description gives the essential behavioral promise — AI answer with cited sources — and the main targeting/emulation options. It could be more explicit about the optional include flags, but the schema covers those details.

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 each parameter. The description adds no new per-parameter meaning beyond summarizing location/UULE and device emulation, which are already in the schema.

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 uses a specific verb and resource: 'Submit a prompt to Google AI Mode... and return the AI answer with cited sources.' This clearly identifies what the tool does and distinguishes it from sibling tools like scrape_google or scrape_gemini.

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 conveys the tool's intended use by naming Google AI Mode and its key capabilities, such as location/UULE targeting and device emulation. However, it does not explicitly state when to choose this tool over alternatives or when not to use it.

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

A4.3/5.0
Disambiguation5/5

list_countries and list_states are clearly metadata helpers, while each scrape_* tool targets a specific engine or search vertical. Even the Google-family tools are differentiated by output type: organic results, AI Mode answers, and news.

Naming Consistency5/5

All tools use snake_case verb_noun naming: list_* for metadata and scrape_* for engine-specific operations. Longer names like scrape_google_ai_mode still follow the same pattern with no mixed conventions.

Tool Count5/5

With 10 tools, the set is well-scoped and each tool earns its place. The two list tools support the eight distinct scraping targets without unnecessary redundancy.

Completeness4/5

The core workflow—choose a country/state and scrape an engine—is well covered across major AI engines and Google verticals. However, there is no way to enumerate supported engine models even though list_countries accepts a model parameter, and some obvious Google verticals like images or shopping are absent.

Resources