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

Annotations already declare readOnlyHint=true and destructiveHint=false, which the description doesn't contradict. The description adds model semantics but no behavior beyond generating a report, such as return format or data aggregation details.

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?

One sentence plus a bulleted list of models. Efficient and front-loaded, though the model definitions could be considered redundant with the enum, but they add meaning.

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?

The tool is simple and read-only, but the description omits what the report contains, date range semantics, and any caveats about data-driven attribution. With no output schema, a little more detail on return value would help.

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 covers model and conversionType with brief descriptions, leaving startDate/endDate undescribed. The description adds definitions for each attribution model, which is highly useful and goes beyond the schema's placeholder 'Attribution model'.

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 generates an attribution report showing channel contribution to conversions. The term 'attribution' distinguishes it from general analytics tools like get_analytics, though it doesn't explicitly name 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?

No guidance on when to choose this over get_analytics, get_unified_analytics, or get_benchmark_comparison. The model list is relevant but doesn't provide selection criteria or exclusions.

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