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Google AI Answer

google_ai_answer
Read-only

Return a Google AI Mode answer with sources for a question. For organic result lists use google_search. Required: query.

Input Schema

NameRequiredDescriptionDefault
queryYesQuestion for AI-assisted search.
countryNoTwo-letter country code used to localize the answer.
languageNoTwo-letter language code for the answer.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so the safety and reach profile is covered structurally. The description adds that results come with sources, which is useful, but says nothing about latency, cost, rate limits, or whether an AI answer might be absent for some queries. With annotations carrying the main burden, this is adequate but thin.

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?

Three short sentences, zero filler, with the core capability front-loaded before the sibling disambiguation and the required-parameter note. Every clause 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?

Annotations cover safety, the schema fully covers all three parameters, and there is no output schema to explain. The description hints at the return shape ("answer with sources"), which is enough for a simple three-parameter read tool, though a note about answer unavailability or localization effects would close the last gap.

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%, and the country/language enums are fully self-documenting, so the schema does the heavy lifting. The description only restates "Required: query," which duplicates the schema's required array and adds no new meaning about how query phrasing affects the answer.

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 ("Return a Google AI Mode answer with sources") and immediately distinguishes it from the nearest sibling, google_search, by output type. An agent can pick between the two without opening either 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?

Explicitly names the alternative tool and the condition that selects it: "For organic result lists use google_search." That is a concrete when/when-not routing rule rather than implied usage.

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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