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

Annotations already disclose read-only, idempotent, non-destructive, closed-world behavior. The description adds meaningful behavioral context beyond that: the data comes 'from Amazon's own Sponsored Brands report' and it 'Says whether SB was collected at all for this store — no campaign and never collected are different answers.' This clarifies an important data-availability nuance for interpreting results.

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 and covered metrics, then adds the key collection nuance, then use cases. Every sentence carries useful information and there is no filler.

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?

For a read-only reporting tool with rich annotations, three fully described optional parameters, and no output schema, the description covers what it reports and the important distinction between 'no campaign' and 'never collected.' It could go one step further by describing the response shape or row granularity beyond 'per campaign,' but it is otherwise complete enough to invoke 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 the schema already documents days (range and default), account, and marketplace_id. The description only indirectly references 'over a window' and adds no format, constraints, or selection guidance beyond what the schema provides. Baseline 3 is appropriate.

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 names a specific resource and metrics: 'Sponsored Brands campaigns ... spend, sales, ACoS, orders, and new-to-brand orders and sales per campaign over a window, from Amazon's own Sponsored Brands report.' It is clearly a read-reporting tool for a defined ad product. It does not explicitly name sibling alternatives such as get_sponsored_display or get_ads_campaigns, so the sibling differentiation is implicit rather than stated.

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 gives concrete example questions the tool answers: 'how are my Sponsored Brands ads doing' and 'how many new customers do my brand ads bring.' That establishes context for use. However, it offers no when-not-to-use guidance and does not point to alternatives, so it stops short of the highest bar.

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