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google_ads_search_terms_review

Score every search term against six rules to split them into add, exclude, or watch candidates. Provides detailed metrics and reasons for each candidate to optimize campaign performance.

Instructions

Score every search term in a Google Ads campaign against six hardcoded rules and split them into add / exclude / watch buckets. Returns {campaign_id, ad_group_id, period, target_cpa, target_cpa_source, add_candidates, exclude_candidates, watch_candidates, summary:{total_search_terms, add_count, exclude_count, watch_count}, intent_analysis?}. Each candidate has {search_term, action, match_type ('EXACT'|'PHRASE'), score (40-90), reason, metrics:{conversions, clicks, cost, ctr}}. target_cpa is resolved from the explicit argument first, then the campaign's bidding strategy, then last-30-days actual CPA; target_cpa_source reports which path ('explicit'|'bidding_strategy'|'actual'|'none'). New terms absent from the previous period are routed to watch_candidates. Read-only — emits candidates but does not add or exclude anything. Default period is LAST_7_DAYS. For keyword/N-gram overlap stats use google_ads_search_terms_analyze; for the raw query log use google_ads_search_terms_report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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.
campaign_idYesCampaign ID as a numeric string without dashes (e.g. '23743184133'). Obtain via google_ads_campaigns_list.
periodNoReporting window for the metrics. Default 'LAST_7_DAYS' — this tool is tuned for short-horizon comparison. Use LAST_14_DAYS or LAST_30_DAYS for longer baselines.
target_cpaNoOptional explicit target CPA in account currency (e.g. 3000 = ¥3,000). Exclusion rule 4 fires at cost >= target_cpa * 2. Falls back to the campaign's bidding strategy target, then last-30-days actual CPA; if none can be resolved, CPA-gated rules are skipped.
Behavior4/5

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

With no annotations, the description effectively carries the transparency burden. It declares read-only, details the CPA resolution fallback, notes new terms are routed to watch_candidates, and explains the scoring context (six hardcoded rules). Missing rate limits or error handling, but that is not expected for all tools.

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?

The description is dense and informative, but slightly long. It front-loads the purpose and then methodically describes the output structure. Every sentence earns its place, but could be more concise by grouping related details.

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?

For a tool with 4 parameters and complex output, the description explains the return structure in detail, including all candidate fields and the summary object. It covers default period and sibling differentiation. Lacks explanation of the six rules, but that is manageable for an agent.

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%, baseline 3. The description adds value by explaining target_cpa fallback order, period recommendation ('tuned for short-horizon comparison'), and customer_id fallback from credentials. This goes beyond simple schema descriptions.

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 starts with a specific verb ('Score every search term') and clearly identifies the resource ('Google Ads campaign'), split into buckets. It explicitly distinguishes from siblings by naming alternative tools (google_ads_search_terms_analyze, google_ads_search_terms_report).

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 clearly states the read-only nature and defaults (e.g., period=LAST_7_DAYS). It explicitly calls out when to use sibling tools: 'For keyword/N-gram overlap stats use...; for the raw query log use...' This gives good context, though it doesn't list negative when-not scenarios.

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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