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

With only openWorldHint annotation, the description adds meaningful behavioral context by enumerating included metric categories, excluding non-category indicators, and specifying it queries periodic changes. It does not fully disclose data freshness or pagination, but no annotation contradiction exists.

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 structured with sections for purpose, exclusions, metrics, and examples, making it scannable. It is somewhat long due to the metric enumeration and pricing detail, but each part contributes useful information.

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?

For a 2-parameter tool with an output schema, the description is thorough: it explains scope, boundaries, metrics, and example queries. It does not discuss ambiguity handling or data source limitations, but these are minor gaps given the output schema covers return values.

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?

Input schema covers 100% of parameters, and the description adds value with typical question examples that demonstrate parameter format (country prefix plus company name), reinforcing the schema's short 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 it queries periodic reputation/brand changes for a specific company, listing concrete metrics (international media coverage, research reports, social platform heat, credit ratings, awards, public opinion health). It distinguishes from siblings by explicitly excluding domestic-only awareness/favorability and batch filtering by park/industry chain.

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?

Provides explicit when-to-use context (international reputation/brand metrics) and when-not-to-use exclusions (domestic-only metrics, batch filter lists). Does not name specific alternative sibling tools, but the typical question patterns give clear guidance on input formulation.

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