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

The description adds meaningful behavioral context beyond the openWorldHint annotation by disclosing that results merge total/production/sales/dependent lists and that other enterprise classifications are excluded. Since the annotations do not already describe return composition or exclusions, this is valuable transparency; no 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 moderately long but front-loaded with the core purpose, then organized into indicators, exclusions, and typical examples. The structure makes it skimmable, and the pricing block is additional metadata rather than redundant explanation.

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 three-parameter list query with an output schema and openWorldHint annotation, the description covers the key selection criteria, return categories, exclusions, and representative questions. It is complete enough for an agent to select and invoke the tool correctly, though naming the count-only sibling explicitly would have made it stronger.

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 input schema already documents all three parameters with examples, so the baseline is 3. The description adds typical question patterns and mentions region granularity, but it does not significantly extend the semantics of the parameters 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 states the tool queries a list of companies surviving more than 5 years based on a specific region and industry chain, using the specific verb '查询' and a well-defined resource. It also differentiates itself from count-only tools by explicitly excluding '仅返回数量不返回名单', though it does not directly name sibling alternatives.

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 with typical question examples and specifies exclusions such as other enterprise classifications and count-only results. It does not explicitly name alternative tools like chain_year5_company_num, but the negative guidance and examples make the intended use reasonably clear.

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