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google_ads_creative_research

Collect landing page analysis, top-performing ads, search term insights, and keyword suggestions to draft or refresh Google Ads creative for a campaign.

Instructions

Collect every input an LLM needs to draft or refresh Google Ads creative for a single campaign. Returns {campaign_id, url, lp_analysis (same shape as google_ads_landing_page_analyze), existing_ads:[{ad_id, headlines, descriptions, final_urls, impressions, clicks, conversions, ctr}] (top 5 RSA ads by impressions, REMOVED excluded), search_term_insights:{high_cv_terms (top 10 by conversions), high_click_terms (top 10 by clicks), total_terms}, keyword_suggestions (KeywordPlanIdeaService output for up to 5 seeds derived from LP title + h1 + meta_description), existing_keywords (list_keywords output), context_summary (string)}. Any failing sub-step is replaced with the literal string 'fetch_failed' so the envelope never raises. Side effect: one outbound LP fetch (same SSRF policy as google_ads_landing_page_analyze) plus several GAQL queries. For just the LP use google_ads_landing_page_analyze; for just RSA asset diagnostics use google_ads_rsa_assets_analyze.

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.
urlYesAbsolute landing page URL to analyze (http:// or https:// only, e.g. 'https://example.com/lp/'). SSRF-protected — private-range, loopback, and cloud-metadata hosts are rejected.
ad_group_idNoOptional ad group ID as a numeric string (e.g. '145680123456') to restrict results to a single ad group. Omit to include every ad group matching the campaign filter.
Behavior5/5

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

No annotations are provided, so the description bears full transparency burden. It discloses all behavioral traits: outbound LP fetch with SSRF policy, GAQL queries, failure handling without raising exceptions, and the exact return structure. This is thorough and leaves no ambiguity.

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 but every sentence adds necessary detail. It is front-loaded with the primary purpose and structured logically (output, error handling, side effects, alternatives). Slightly long but efficient; earns a 4.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complex return type (nested objects from multiple sub-sources) and no output schema, the description completely specifies every return field, its source, and shape. Side effects and error handling are also fully covered. No gaps remain.

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% with parameter descriptions. The description adds value beyond the schema by noting that customer_id can fall back to environment variables and that url is used for LP analysis. This extra context justifies a 4, above the baseline 3.

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's purpose: 'Collect every input an LLM needs to draft or refresh Google Ads creative for a single campaign'. It specifies the exact output structure and differentiates from sibling tools by naming alternatives (google_ads_landing_page_analyze, google_ads_rsa_assets_analyze).

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 usage guidance: when to use (comprehensive creative research for one campaign) and when not (for LP only or RSA diagnostics), with direct references to alternative tools. It also explains error handling (fetch_failed substitution) and side effects.

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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