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

Annotations are sparse (only openWorldHint), so the description carries the transparency burden. It explains the output as a combined text with multiple counts and notes exclusions, but does not detail computation specifics or edge cases. This is adequate but not deep.

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 well-structured with a core statement, metric enumeration, exclusions, and examples. It is front-loaded and each section adds value, though it includes a pricing line that is extraneous but not harmful.

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 existence of an output schema, the description appropriately avoids detailing return values. It covers the query context, parameters, metrics, exclusions, and examples, making it sufficiently complete for this count-oriented tool.

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 descriptions already cover all three parameters at 100%. The description adds example queries that clarify usage of region, chain_name, and year, but does not significantly enrich parameter meaning beyond what the schema provides.

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 defines the purpose: query the count of companies that won tenders in the last two years based on region and industry chain. It explicitly lists the metrics (total, production, sales, dependency) and what is excluded (other categories, company list), distinguishing it from list tools and other count tools.

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 includes typical questions and states what is not included (e.g., company list details), which implicitly guides when to use this count tool instead of a list tool. However, it does not explicitly name alternative tools or provide explicit when-not-to-use conditions.

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