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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds substantive behavioral context by enumerating exactly what the call returns, which is important because there is no output schema. It does not reveal any hidden side effects or edge-case behavior, but for a read-only evaluation tool this is strong transparency.

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 two sentences with no wasted words. The first sentence packs the core purpose and a dense but necessary output list (no output schema exists to carry that burden), and the second sentence provides crisp usage triggers. Every clause earns its place.

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 27-parameter tool with 22% schema coverage and no output schema, the description gives a solid picture of the primary output and when to call it, but it leaves the many optional filters unexplained. It is adequate for a simple 'evaluate this brand' call but incomplete for understanding filtering behavior, marketplace defaults, or how top-ASIN selection reacts to constraints.

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 only 22%, with 21 of 27 parameters undocumented. The description does not compensate: it only mentions 'composite sourcing score' as the ordering criterion and never explains the min/max filters, distribution_verdict_in behavior, or product_title_contains semantics. An agent would have to guess at the meaning of most optional parameters.

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: 'Evaluate a brand for portfolio inclusion,' which clearly distinguishes this tool from sibling tools like evaluate_asin_sourcing and evaluate_category_for_private_label. The detailed return-value list reinforces that it is a brand-level evaluation, not an ASIN- or category-level one.

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 gives explicit trigger phrases: 'Use when the user names a brand and asks ...' with four concrete example queries. This tells an agent when to invoke the tool, but it does not explicitly state when not to use it or point to alternatives, so it stops short of a 5.

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