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MaxGhenis

Google Ads MCP Server

by MaxGhenis

execute_query

Run GAQL queries to retrieve Google Ads campaign, ad group, keyword, and performance metrics from your account.

Instructions

Execute a Google Ads Query Language (GAQL) query.

Use this for any read operation - campaigns, ad groups, keywords, ads, metrics, etc.

Common queries:

  • Campaigns: SELECT campaign.id, campaign.name FROM campaign

  • Ad groups: SELECT ad_group.id, ad_group.name FROM ad_group

  • Keywords: SELECT ad_group_criterion.keyword.text FROM ad_group_criterion

  • Metrics: SELECT campaign.name, metrics.clicks FROM campaign

Args: query: The GAQL query to execute customer_id: Target customer ID (optional if set in config). Use digits only, no dashes.

Returns: List of result rows as dictionaries

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
customer_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, description carries burden. It correctly labels as read operation, but does not cover potential errors, rate limits, or pagination behavior. Returns format is briefly explained.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise with useful examples. Could omit some redundancy, but overall well-structured and front-loaded with purpose.

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 no annotations and presence of output schema, description adequately covers basic usage. Lacks detail on error handling and result limits, but sufficient for typical agent invocation.

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?

Schema coverage is 0%, so description compensates by explaining query as GAQL string with examples, and customer_id as optional digits-only. Provides meaningful context beyond 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 clearly states the tool executes GAQL queries for read operations, provides common query examples, and distinguishes from sibling tools which are all write-oriented.

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?

Explicitly says 'use for any read operation' and implies not for mutations via sibling tool list. Provides examples and optional parameter explanation, but lacks explicit when-not-to-use guidance.

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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