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samtactical

google-ads-mcp

by samtactical

get_ad_group_performance

Retrieve ad group performance metrics (impressions, clicks, cost, conversions) for a customer ID and date range, with an optional campaign filter to focus on specific campaigns.

Instructions

Get ad group-level performance metrics (impressions, clicks, cost, conversions).

Args: customer_id: The Google Ads customer ID (10 digits, dashes optional). date_range: A GAQL date range literal, e.g. LAST_30_DAYS, LAST_7_DAYS, THIS_MONTH. campaign_id: Optional campaign ID to restrict results to a single campaign.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
date_rangeNoLAST_30_DAYS
campaign_idNo
customer_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It does not mention read-only behavior, authentication requirements, pagination, rate limits, or any side effects. Although 'get' implies a read operation, the description offers no explicit behavioral context beyond its purpose.

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 concise: one sentence for purpose followed by a compact argument list. Every line adds value, and the purpose is front-loaded. No fluff or redundancy.

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?

The description covers all parameters and the core purpose, and the output schema supplies return details. It is functionally complete for invocation. A minor gap is the lack of explicit usage guidance relative to siblings, but that is covered under usage_guidelines; overall completeness is high.

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

Parameters5/5

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

The schema has zero description coverage, so the description must compensate, and it does thoroughly. It explains customer_id format (10 digits, dashes optional), date_range as a GAQL literal with examples, and campaign_id as an optional filter. This adds meaning well beyond the bare schema properties.

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 (Get) and resource (ad group-level performance metrics), listing the exact metrics (impressions, clicks, cost, conversions). This clearly distinguishes it from sibling tools like get_campaign_performance and get_keyword_performance by the aggregation level.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool versus its alternatives. The purpose implies 'use for ad group metrics', but there is no mention of exclusions or conditions (e.g., 'for campaign-level metrics use get_campaign_performance'). The usage context is inferred rather than stated.

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