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google_ads_recommendations_list

Retrieve Google Ads automated recommendations with type, impact metrics, and campaign context. Filter by campaign or recommendation type to focus results.

Instructions

List Google's current automated recommendations for the account. Returns [{resource_name, type (RecommendationType enum string, e.g. 'KEYWORD', 'TEXT_AD', 'TARGET_CPA_OPT_IN', 'MAXIMIZE_CONVERSIONS_OPT_IN'), impact:{base_metrics:{impressions, clicks, cost_micros}}, campaign_id (resource path when scoped to a campaign)}]. Read-only. Filter by campaign_id to scope to one campaign, or by recommendation_type to scope to one kind. To apply a recommendation use google_ads_recommendations_apply with resource_name from this list.

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_idNoOptional campaign ID as a numeric string. Omit to list account-wide recommendations.
recommendation_typeNoOptional RecommendationType enum string (e.g. 'KEYWORD', 'TEXT_AD', 'TARGET_CPA_OPT_IN'). Validated against the client's allow-list before GAQL embedding.
Behavior4/5

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

No annotations provided, but description discloses read-only behavior and output structure. Does not mention auth details or rate limits, but overall sufficient for a list tool.

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?

Two sentences, front-loaded with main action. Output structure embedded in first sentence could be more structured, but still concise.

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?

No output schema, but description includes output shape. Covers filtering and fallback. Missing edge cases, but adequate for a list tool with 3 params.

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?

Schema has 100% coverage, but description adds context: customer_id fallback, filtering options, recommendation_type validation and example values.

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?

Description clearly states the tool lists Google's automated recommendations and details the output structure. It distinguishes from sibling tools by mentioning google_ads_recommendations_apply for applying recommendations.

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?

Explicit guidance on when to use (listing), how to filter (by campaign_id or recommendation_type), and when not to (use apply tool instead). Also notes read-only nature.

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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