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eneelkant

google-ads-mcp

by eneelkant

execute_gaql

Read-only

Run any Google Ads Query Language (GAQL) query to fetch custom campaign, ad group, keyword, and performance data. Use it for reports and metrics not covered by other tools.

Instructions

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

This is the most flexible tool -- use it for any read query not covered by other tools. GAQL reference: https://developers.google.com/google-ads/api/docs/query/overview

Args: customer_id: Google Ads customer ID. query: Full GAQL query string (SELECT ... FROM ... WHERE ...).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
customer_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior3/5

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

The readOnlyHint and openWorldHint annotations already convey the safety and scope profile, and the description aligns by saying 'read query' and 'most flexible.' It adds some context about arbitrary query execution, but does not disclose potential behavior like pagination, result shape, or query limits. This is acceptable but not rich given annotation coverage.

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, front-loaded with the core purpose, and the Args section organizes parameter details clearly. Every sentence contributes useful information, including the external GAQL reference link.

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?

Given the tool's arbitrary-query nature, empty schema descriptions, and lack of output schema, the description covers the essentials: purpose, fallback usage, arguments, and a reference link. It is slightly incomplete in not mentioning output shape or query limits, but the external reference and generic nature mitigate this.

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

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description compensates by explaining both parameters: customer_id as a Google Ads customer ID and query as a full GAQL string with SELECT/FROM/WHERE structure. It adds real meaning beyond the bare schema, though the customer_id format is left underspecified.

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 tool executes an arbitrary GAQL read query, using a specific verb and resource. It explicitly positions itself as the flexible catch-all for queries not covered by other tools, which differentiates it from the large sibling tool set.

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?

It provides explicit selection guidance: use this tool for any read query not covered by other tools. This both states when to use it and implies when not to use it, namely when a dedicated tool already exists for the query.

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