attribution_check

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

Annotations already indicate read-only and non-destructive. The description adds value by detailing the analysis scope (consent gaps, attribution models) and behavior of the conversion_events parameter. No contradictions.

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 front-loaded with purpose, then specific diagnoses, then parameter details. It is concise (5-6 lines) with no fluff, though could group related points.

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?

With an output schema present, return values need not be explained. The description covers main behavioral aspects and key parameter semantics, but omits context for customer_id and property_id. Reasonably complete for an analysis tool.

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 coverage is 0%, so the description must compensate. It explains conversion_events (optional list vs aggregate) and date format/empty default, but does not explain customer_id or property_id. Adds partial value.

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 clearly states the tool compares Ads-reported conversions to GA4 to find tracking discrepancies, with specific diagnoses (GDPR consent gaps, attribution model differences, missing events). This distinguishes it from sibling analysis tools like validate_tracking or analyze_campaign_conversions.

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?

It provides clear context for when to use (comparing Ads vs GA4, diagnosing specific issues) but does not explicitly mention when not to use or contrast with alternatives like get_tracking_events or analyze_campaign_conversions.

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