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by dev.cloro

Scrape Google Gemini

scrape_gemini
Read-only

Submit a prompt to Google Gemini from a chosen country (and optionally US state) and return the answer with cited sources. Use this to see how Google Gemini answers a prompt and which brands/sources it mentions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNoOptional state code for state-level targeting (e.g. "CA" when country is "US"). Only some countries support this — call list_states for the supported countries and their codes.
promptYesThe prompt to submit to Google Gemini.
countryYesISO 3166-1 alpha-2 country code to geo-target the request from (e.g. "US"). Use list_countries to see supported codes per model.
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.

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. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare read-only, open-world, and non-destructive behavior. The description adds useful context about geo-targeting and cited-sources output, but it does not disclose operational details such as response format variety or potential limitations. This modest addition beyond annotations supports a 3.

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 concise sentences that front-load the core action and result before adding the use case. It contains no filler, no repetition of schema details, and every sentence earns its place.

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 schema fully documents all input parameters and the nested include flags, while annotations cover the safety profile. The description supplies the missing output clue (answer with cited sources, brands) and a concrete use case, making selection and invocation reasonably clear despite the absence of an output schema.

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?

The input schema has 100% parameter description coverage, including prompt, country, state, and the nested include object. The description only loosely echoes the schema ('chosen country', 'optionally US state') without adding new syntactic or semantic detail, so the 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 states a specific verb and resource: 'Submit a prompt to Google Gemini' from a chosen country and return the answer with cited sources. It names the model explicitly and focuses on brands/sources, which differentiates it clearly from sibling scrapers like scrape_chatgpt and scrape_perplexity.

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 second sentence gives a clear intended use case: 'see how Google Gemini answers a prompt and which brands/sources it mentions.' This provides enough contextual guidance for an agent to know when to choose this tool, though it does not explicitly mention when not to use it or name alternatives.

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