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kLOsk

Google Ads - AdLoop

by kLOsk

run_gaql

Read-only

Run custom GAQL queries for advanced Google Ads data retrieval not covered by standard tools, with output in table, JSON, or CSV format.

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
customer_idNo
formatNotable

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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 that the output can be formatted as 'table', 'json', or 'csv', which provides useful behavioral context. However, no additional behavioral traits beyond the annotations are disclosed, hence a baseline score.

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 very concise: three sentences that cover purpose, usage guidance, and format options. Every sentence earns its place without redundancy, making it easy to parse.

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

Completeness3/5

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

Given the presence of an output schema, the description doesn't need to explain return values. It covers the format parameter and references syntax help, but omits details on 'customer_id' and potential errors or limits. For a flexible query tool, more completeness would be beneficial, but it's adequate for basic use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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 explains the 'format' parameter with its three options, but provides no details on 'query' (beyond stating it's a GAQL query) or 'customer_id'. This partial coverage adds some value but insufficiently documents all parameters, resulting in a low score.

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 clearly states the verb 'Execute' and the resource 'arbitrary GAQL query'. It distinguishes itself from sibling tools by noting it's for 'advanced queries not covered by the other tools', making its purpose unambiguous.

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 description advises using this tool for advanced queries not covered by others, providing clear when-to-use context. It also references GAQL syntax rules for help. While it doesn't explicitly list when not to use (e.g., for standard queries), the guidance is sufficient for an AI agent to make appropriate decisions.

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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