google_ads_creative_research

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

With no annotations provided, the description carries the full burden and succeeds. It discloses a side effect ('one outbound LP fetch (same SSRF policy as google_ads_landing_page_analyze) plus several GAQL queries') and a failure-handling behavior ('Any failing sub-step is replaced with the literal string 'fetch_failed' so the envelope never raises'). This gives the agent critical expectations about safety, reliability, and non-raising behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence (and even clause) contributes meaningful information: purpose, return shape, failure behavior, side effects, and alternatives. There is no fluff or repetition. The structure is compact despite the complexity, and it front-loads the core purpose.

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

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

This is a complex composite tool with no output schema, so the description must compensate by fully explaining the return value, and it does so with a detailed inline JSON-like structure. It also covers side effects, failure semantics, and alternatives. For a tool of this complexity, the description is complete enough for an agent to select and invoke it correctly.

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

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

Schema description coverage is 100%, so each parameter (id, URL, ad_group_id, customer_id) is already well documented in the schema. The description adds no additional parameter-level semantics beyond what the schema states; it focuses on output structure. Per the rubric, baseline 3 applies when schema does the heavy lifting, and there is no extra value here to push higher.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Collect every input an LLM needs to draft or refresh Google Ads creative for a single campaign.' It enumerates the exact contents of the return object, making the tool's scope unmistakable. It also distinguishes itself from siblings by naming google_ads_landing_page_analyze and google_ads_rsa_assets_analyze as more targeted alternatives.

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

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

The description explicitly states when to use this tool versus alternatives: 'For just the LP use google_ads_landing_page_analyze; for just RSA asset diagnostics use google_ads_rsa_assets_analyze.' It also frames the tool as a single-campaign creative research aggregator, clarifying the intended use case. This is the gold standard for usage guidance.

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