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

The description discloses what the tool returns (total count and breakdowns by company type) and what it does not include (other classifications and detailed lists). It also mentions it is a query (查询), implying read-only behavior. The annotation only includes openWorldHint, so the description adds useful behavioral context beyond annotations.

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: it states the core function, lists relevant metrics, exclusions, examples, and pricing in a logical order. It is slightly verbose but avoids redundancy, and each sentence adds value.

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?

The description thoroughly covers the tool's purpose, parameters, return value (counts and breakdowns), exclusions, and provides example queries. Even though an output schema exists, the description adds meaningful detail about the return content, making the tool self-explanatory for typical use cases.

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?

Each parameter is explained with concrete examples: year (e.g., 2024) explicitly marked as optional, region (e.g., 全国, 成都, 海淀区), and chain_name (e.g., 集成电路, 新能源, 人工智能). This goes beyond the schema descriptions by providing realistic usage examples and clarifying the optionality of year.

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 function: to query the number of companies with copyright (软著) based on region and industry chain. It distinguishes from sibling tools that return lists or focus on other attributes like patents, and explicitly mentions that it returns counts, not detailed lists.

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 typical questions and explicitly states exclusions ('不包含：其他企业分类的统计；企业名单明细'), which implicitly guides when to use this tool vs. the list variant. It could be more explicit about when to choose this over other _num tools, but the attribute-specific naming and examples make it clear enough.

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