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google ads validate query

google_ads_validate_query
Read-onlyIdempotent

Validate metric, dimension, and resource compatibility before running a Google Ads query to prevent errors from segment restrictions or unavailable resources.

Instructions

Validate metric/dimension/resource compatibility BEFORE executing a query. Checks segment restrictions and resource availability.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
metricsYesMetric keys to validate
resourceNoNative Google Ads resource from which the GAQL query selects rows.campaign
dimensionsNoDimension keys to validate

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYesOriginal tool result: parsed JSON when the text is JSON, otherwise the text or multiple MCP content blocks. Provider fields depend on the selected query.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.1

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior; the description adds meaningful behavioral detail by explaining that it checks segment restrictions and resource availability. This goes beyond the safety hints and clarifies what the validation actually covers.

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 short sentences with no filler. The key action and purpose are front-loaded in the first sentence, and the second adds concrete detail about what is checked without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a low-complexity validation tool with a fully described input schema, a rich output schema, and comprehensive annotations, the description provides all essential context. It clearly communicates the tool's role, timing, and scope, so an agent should be able to invoke it correctly.

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 already documents metrics, resource, and dimensions. The description adds that these fields are validated for compatibility, but it does not explain parameter-specific constraints, formats, or examples beyond 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 states a specific verb ('Validate') and a precise object: metric/dimension/resource compatibility before executing a query. It clearly distinguishes this from sibling tools like google_ads_run_gaql or the read-only getters.

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 phrase 'BEFORE executing a query' explicitly signals when to use this tool, establishing it as a preflight check for GAQL execution. It does not explicitly name sibling alternatives or provide when-not-to-use conditions, but the timing and intended context are clear.

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