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google_ads_search_terms_report

Retrieve raw search terms that triggered your ads with performance metrics. Filter by campaign or ad group to analyze actual user queries.

Instructions

List actual user search queries that triggered ads in the account over a reporting window. Returns one row per search term shaped as {search_term, metrics}, where the metrics object contains impressions, clicks, cost_micros, cost (currency-formatted), conversions, and ctr. The rows are filterable by campaign_id and/or ad_group_id but those IDs are NOT echoed back in the output — scope your query before calling. Read-only. Use this for raw query logs when you need to eyeball the terms yourself. For rule-based add/exclude candidates use google_ads_search_terms_review; for intent-class distribution use google_ads_search_terms_analyze; for campaign-level aggregates without query breakdown use google_ads_performance_report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodNoReporting window for the metrics. Default 'LAST_30_DAYS'. Use a shorter window (LAST_7_DAYS / LAST_14_DAYS) when diagnosing recent changes; use LAST_90_DAYS for trend baselines.
ad_group_idNoRestrict results to a single ad group by numeric ID. Omit to include all ad groups under the campaign filter (or the entire account if campaign_id is also omitted).
campaign_idNoRestrict results to a single campaign by numeric ID. Omit to include all campaigns.
customer_idNoGoogle Ads customer ID as a 10-digit string without dashes (e.g. '1234567890'). Optional — falls back to GOOGLE_ADS_CUSTOMER_ID / GOOGLE_ADS_LOGIN_CUSTOMER_ID from the configured credentials when omitted.
Behavior4/5

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

Since no annotations are provided, the description carries full burden. It declares read-only and describes output shape. However, it lacks details on data latency, pagination, or row limits, which would improve transparency.

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 a single, well-organized paragraph with no wasted words. It front-loads the main purpose and efficiently covers output, filtering, and sibling differentiation.

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 4 parameters, no output schema, and many sibling tools, the description is largely complete. It explains output shape, filtering behavior, and when to use. Minor gap: no mention of data latency or limitations.

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 100%, so parameter meanings are already clear. The description adds useful context: filter IDs not echoed back, and guidance on choosing period values (e.g., shorter windows for recent changes).

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 lists actual user search queries that triggered ads, and explicitly distinguishes it from three sibling tools (review, analyze, performance_report) with specific use cases.

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?

The description provides explicit when-to-use guidance ('for raw query logs when you need to eyeball the terms yourself') and names alternatives for different needs. It also warns that filter IDs are not echoed back.

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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