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Glama

google_search

Run raw Google searches and return structured SERP data, including organic rankings, People Also Ask, and related searches. Use it to analyze Google ranking and position data directly.

Instructions

Run a raw Google search and get back structured SERP data: organic results, People Also Ask, related searches. Use this when you need Google's ranking/position data itself, not just page content (for that, use web_search).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoA single Google search query, e.g. 'apify web scraping'.
deviceNoDevice type to emulate.desktop
queriesNoMultiple queries to run in one call. Overrides `query` when non-empty.
safeSearchNoEnable Google SafeSearch filtering.
countryCodeNo2-letter country code to localise results (gl).us
languageCodeNo2-letter interface language code (hl).en
resultsPerPageNoResults per SERP page.
maxPagesPerQueryNoHow many result pages to fetch per query.
includePeopleAlsoAskNoInclude the People Also Ask box.
includeRelatedSearchesNoInclude related-search suggestions.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose the return shape (organic, PAA, related searches) and that results are raw SERP data, but it omits operational traits an agent would care about: rate limits, quota/cost, caching, or whether the service may be blocked. Adequate but incomplete for a zero-annotation tool.

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?

Two tight sentences with the capability front-loaded and the alternative routing second. No filler, though the return-value enumeration slightly overlaps with what the tool obviously returns.

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?

Ten parameters are fully covered by the schema and there is no output schema, so the description's enumeration of return fields is the right level of detail. Minor gaps in pagination/quota behavior are covered adequately by the maxPagesPerQuery and resultsPerPage schema entries.

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 all ten parameters (query/queries override behavior, device, countryCode, languageCode, resultsPerPage, maxPagesPerQuery, include* flags) are already documented in the schema. The description adds no parameter-level meaning, which matches the baseline 3 when the schema does the heavy lifting.

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?

States a specific verb and resource ('Run a raw Google search') and enumerates the returned SERP structures (organic results, People Also Ask, related searches). It also explicitly differentiates from the sibling web_search, so an agent can route correctly without opening a schema.

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

Usage Guidelines5/5

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

Explicit when-to-use condition ('when you need Google's ranking/position data itself, not just page content') paired with the named alternative ('for that, use web_search'). Both the selection condition and the fallback are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.