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Google Ads - AdLoop

by kLOsk

Custom Google Ads query

run_gaql
Read-only

Execute arbitrary Google Ads Query Language (GAQL) queries to retrieve custom data from Google Ads. Use when other tools lack the specific query you need.

Instructions

Execute an arbitrary GAQL (Google Ads Query Language) query.

Use this for advanced queries not covered by the other tools. See the GAQL reference in the AdLoop cursor rules for syntax help.

format: "table" (default, readable), "json" (structured), "csv" (exportable)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
formatNotable
customer_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.13.2
  2. Removedv0.13.0
  3. First observedv0.9.0

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds the arbitrary-query nature and format options but does not disclose operational behavior such as rate limits, failure modes, or permission requirements. This is adequate given the annotations but not rich.

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 compact and front-loaded: the action, usage context, and format options are stated in a few sentences with no wasted words. The pointer to the GAQL reference is a useful addition.

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 an arbitrary query tool with an output schema and safety annotations, the description covers the key decision points: what it does, when to use it, output formats, and syntax help. The main missing piece is customer_id semantics, but the output schema covers return values.

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 0%, so the description must compensate. It usefully explains the format parameter values ('table', 'json', 'csv') and points to a GAQL reference for query syntax, but it does not describe the customer_id parameter or its expected format. This leaves a gap for one of three parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Execute an arbitrary GAQL query.' It also positions the tool as for 'advanced queries not covered by the other tools,' which differentiates it from siblings at a category level, though it does not name specific sibling tools.

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?

It explicitly says 'Use this for advanced queries not covered by the other tools,' giving a clear usage condition and an exclusion. It could be stronger by naming alternative tools, but the guidance is unambiguous enough.

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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