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

With only openWorldHint annotation, the description carries the transparency burden. It discloses scope boundaries (excludes lists and other classification stats) but does not explain default year behavior, aggregation details, or any caveats about how the count is computed. Basic but not deeply transparent.

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 core description is concise and logically structured with metrics, exclusions, and examples. The embedded Pricing block adds noise and is not directly relevant to selection or invocation, keeping it from a perfect score.

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 simple two-parameter tool with an output schema, the description is sufficiently complete. It covers what is returned conceptually (count), what is not returned, and typical user phrasings. It could be more complete by suggesting the related list tool, but this is not essential.

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%, so the baseline is 3. The description does not add meaningful parameter semantics beyond the schema; the example questions reinforce park_name usage but add little new information.

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's purpose: querying the count of discredited companies for a specific park (失信人企业数量). It distinguishes itself from the list variant by explicitly excluding 企业名单明细, and provides concrete example questions like 中关村软件园失信人企业有多少.

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 gives clear scope—park-specific discredited enterprise counts—and explicitly excludes other enterprise classifications and list details. It offers typical questions showing when to use the tool, though it does not explicitly name alternative tools such as park_discredited_company_list.

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