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

Annotations already declare this a read-only, idempotent, non-destructive, closed-world call, so the safety profile is covered. The description still adds real behavior: ranked ordering, a default cap of 25 items, brandLimit to request more, and brandsTotal to reveal the untruncated count — the key signal that a result may be abbreviated. It does not address auth or rate limits, but nothing suggests those are relevant here.

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?

Front-loaded with the usage trigger, then inputs, then returns, then next steps — a sensible order with no filler. The return sentence is long and clause-heavy ('with the dates and the number of answers counted'), which slightly hurts scanability.

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?

There is no output schema, and the description compensates fully: it enumerates the returned fields (name, market, constituent questions, mentioned brands in rank order, dates, answer counts) and flags truncation via brandLimit/brandsTotal. An agent knows both what to pass and what to expect back.

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 100%, so the schema already defines both parameters and would justify a baseline 3. The description goes beyond it by explaining the relationship between brandLimit and brandsTotal (one requests more, the other reports how many are ranked) and by framing the topic argument as an id from parse_search or a question slug — useful context the schema states more tersely.

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?

States a specific verb (get) and resource (a topic), plus the intent behind it ('which brands AI recommends for something buyers ask about'). It is clearly distinct from parse_search (which supplies the id) and parse_get_brand (which drills into one brand).

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?

Opens with an explicit trigger ('Use when the user asks...'), explains both accepted identifying forms (topic id from parse_search, or a question's slug for the topic behind a question), and closes with concrete next steps: parse_check_brand for a brand or parse_search on the market for neighbors.

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