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

Beyond the readOnlyHint/destructiveHint annotations, the description discloses a nontrivial behavioral trait: the tool reports first observation, not confirmed first entry, and explains that sparse re-sampling can resurface long-present brands as 'new'. This prevents an agent from over-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?

Three sentences, each carrying weight: core definition, critical caveat, and usage examples. The most important semantic warning is front-loaded, and there is no filler or repetition of the schema.

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 description is complete for high-level selection and basic calls through category_name/category_id and since_days, but with 17 parameters and no output schema, there is no coverage of return shape or most filter semantics. It is a viable baseline, not a fully self-sufficient definition.

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 35%, and the description does not compensate by explaining the many undocumented filter parameters (limit, price/asins/seller/buybox filters, first_observed_to, etc.). It adds context about the observation window, but the agent is left without guidance for most of the 17 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 uses a specific verb (Find) with a clearly scoped resource: brands newly observed in a category, defined by the first daily-sampling observation date. The observation-signal caveat distinguishes it from true-entry concepts and separates it from sibling tools like brand_new_asins or operator_new_brands, even without naming them.

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 explicitly lists natural-language triggers ('new brands in Electronics', 'what brands just entered this category', 'category new entrants'), so an agent knows when to select it. It lacks an explicit when-not-to-use clause or a named alternative tool, but the inclusion examples provide clear context.

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