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

Beyond the openWorldHint annotation, the description clarifies the exact indicator coverage (education, age, gender, average tenure) and what is excluded. It gives typical question formats, which helps agents understand likely input-output expectations, though it does not detail response structure or data-period semantics.

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 concise, well-structured, and front-loaded with the core purpose, followed by exclusions and examples. The pricing block is somewhat extraneous but does not significantly hurt readability or clarity.

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 moderate complexity, an output schema, and a large sibling set, the description provides enough context: metrics covered, exclusions, and representative queries. It does not explain the meaning of 'periodic changes' in detail, but the output schema can compensate for return-value specifics.

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 already describes both parameters with coverage at 100%, but the description adds value through typical usage examples and clarifies that company_name and country_name are used together for enterprise-specific queries. The listed question patterns reinforce parameter semantics beyond the minimal schema descriptions.

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 states the tool queries periodic changes in employer-brand metrics for a specific enterprise, focusing on employee education, age, gender, and average tenure. It explicitly excludes entrepreneur image and subjective satisfaction evaluations, distinguishing it from sibling tools like enterprise_change_entrepreneur_image and enterprise_change_employee_evaluation.

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 usage context: use for querying objective employee structure indicators by company name, and explicitly lists exclusions such as not for subjective satisfaction and not for batch screening by park/industry chain. However, it does not name a specific alternative tool to use instead, so it falls 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.