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

With only openWorldHint as an annotation, the description carries the burden of behavioral disclosure. It adds the cost context (40 credits per run) and metric scope, but does not disclose what the 'periodic change' output looks like (trend series, time granularity, data freshness) or any failure constraints. The pricing detail is genuinely useful beyond annotations, but core behavior description is incomplete.

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 organized into labeled sections (涉及指标, 不包含, 典型问法), making it easy to scan. The embedded pricing JSON adds structured cost context. It is slightly dense with partially redundant phrase repetition (the title already conveys 'user brand satisfaction'), but every section 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?

Given the output schema exists, the tool need not explain return values in the description. The description covers purpose, metric boundaries, exclusions, and example usage—sufficient for an agent to discriminate it among ~20 enterprise_change_* siblings. The main remaining gap is clarifying what '周期变化' (periodic change) concretely means in the response, though the output schema partially compensates.

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%—both country_name and company_name have explicit descriptions with examples (比亚迪股份有限公司, Tesla, Inc., 中国). The description's typical question patterns reinforce parameter usage but do not add syntax, formatting, or value-constraint details beyond what the schema already provides, matching the baseline for fully-covered schemas.

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 specifies a concrete action (query periodic changes in user-brand metrics by enterprise) and enumerates the exact metric set (customer satisfaction, after-sales satisfaction, complaint rate, product safety accident rate). It explicitly excludes cognitive indicators like market share and brand awareness, which directly differentiates it from the sibling tool enterprise_change_user_brand_awareness.

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 provides clear inclusion criteria (satisfaction/complaint/safety queries) and explicit exclusion criteria (not for awareness/cognition metrics, not for batch screening by park/industry chain). Example question phrasings demonstrate how to combine country, company, and target metric. However, it does not explicitly name alternative sibling tools to use instead, leaving some ambiguity in a large family of enterprise_change_* tools.

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