crehq_trends_company

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

With no annotations, the description carries the burden of disclosing behavior. It does disclose the return contents (outlet counts, fees/royalties, financial trajectory) and the single-brand scope. However, it omits any operational details such as data freshness, whether prior purchase is required, or how time ranges are determined, leaving some behavioral ambiguity.

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 and front-loaded with concrete deliverables. The second sentence, 'The growth/health curve of a concept in a single call,' is somewhat interpretive but not redundant; it adds a useful mental model. No wasted words, though it could be more action-oriented.

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?

Given no output schema, the description should help the agent anticipate return data. It lists three concrete components (outlet-count history, fee/royalty trends, FDD financial trajectory) and the time-series nature, which is sufficient for a simple one-parameter tool. It does not detail formatting or date ranges, but the provided context is adequate.

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?

The schema has 100% coverage for the single 'id' parameter (described as 'CREHQ company id'). The description reinforces that the tool targets 'ONE brand,' which maps to the id, but adds no additional semantic nuance beyond the schema. Baseline 3 applies since the schema fully documents the parameter.

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 identifies the resource: time-series trends for a single brand, and lists the specific data components (outlet-count history, fee/royalty trends, FDD financial trajectory). It distinguishes from likely siblings by emphasizing 'ONE brand' and a comprehensive 'growth/health curve' in a single call, though it does not 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?

The phrase 'The growth/health curve of a concept in a single call' implies when to use this tool (when a consolidated trend overview for one brand is needed), but it does not provide explicit exclusions or name alternative tools like crehq_trends_geographic. Usage context is present but not sharply defined.

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