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

Annotations only include openWorldHint=true, which is vague. The description clarifies the return is a merged text of total/production/sales/dependent counts, which adds value. It does not mention any additional behavior like sorting, pagination, or error cases, but since the tool is a simple count query, this is acceptable. However, it could be more explicit about the output format (though the output schema 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 concise, front-loaded with the main purpose, and includes exclusions and examples. The pricing info is extraneous but not harmful. It is structured with clear sections, though the pricing block could be removed from the description as it is likely metadata.

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 it has an output schema, the description covers the essential aspects: what it returns (total/production/sales/dependent counts), region granularity, and typical queries. It does not explain edge cases like missing year (it's optional) but the schema does. Sufficiently complete for a simple count 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?

The input schema covers 100% of parameters with descriptions (year, region, chain_name). The description adds nuance by explaining metrics included (生产型/销售型/依赖型) and gives examples of valid region values. However, it does not add major new meaning beyond the schema, so baseline 3 is appropriate.

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 states it queries the count of companies closed in a specific region (country/province/city/district) and industry chain for a given year. It clearly differentiates from sibling tools like chain_close_company_list (list vs. count) and other chain_*_num tools (different metrics like patents, high-tech, etc.). The verb is specific: '查询...企业数量' with clear scope.

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 typical question examples ('典型问法') that illustrate when to use this tool (e.g., '2024年全国集成电路当年注销的企业有多少'). It also states what is not included ('不包含：其他企业分类的统计；企业名单明细'), which helps distinguish from list tools. However, it does not explicitly mention alternatives like chain_close_company_list for when a list is needed, but the exclusion and examples are sufficient.

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