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

The description discloses key behavioral traits: the merged text-style return across four metric dimensions ('合并返回总量/生产型/销售型/依赖型文本') and the exclusion of list details and other classifications. The pricing disclosure (100 credits per run) is a useful operational transparency bonus. Since the openWorldHint annotation carries little behavioral content, the description rightfully bears the disclosure burden, and it does so adequately, though it could add edge-case behavior (e.g., empty results, year handling).

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 tightly structured in four scannable sections: core purpose sentence, included metrics list, exclusions, and typical questions — each earning its place. The metric listing is slightly redundant with the parenthetical '（合并返回...文本）' note, but the examples and pricing block justify the overall length. It is information-dense without being bloated for a tool with this many sibling variants.

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 3-parameter tool with a high sibling count (chain_*/park_* company count/list variants), the description covers the essential ground: query dimensions (region, chain, year), output dimensions (four merged metrics), exclusions, and realistic example phrasings for NL-to-parameter mapping. The presence of an output schema means return-format details are justifiably omitted. Remaining gaps (e.g., terminology explanation of '依赖型' or multi-year behavior) are minor given the well-established sibling pattern.

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% — all three parameters (year, region, chain_name) have clear Chinese descriptions with concrete examples, setting the baseline at 3. The description's example questions do enrich region granularity understanding (全国/成都/海淀区 mapping to country/province/city levels) and show year+chain+region combination patterns, but this is confirmatory rather than additive given the schema already documents each parameter well. The schema carries the weight here.

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 pairs a specific verb (查询/query) with a well-scoped resource: count of patent-owning enterprises filtered by region (country/province/city/district) and industrial chain. It explicitly enumerates the four returned metrics (total/production/sales/dependent) and differentiates from sibling tools by stating what's excluded ('不包含：其他企业分类的统计；企业名单明细'), which directly separates it from the list-family and other classification variants.

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 'Included metrics' and 'Not included' sections clearly bound the tool's scope, and the three 'typical questions' give concrete usage contexts (e.g., '2024年全国集成电路拥有专利的企业有多少' → year/region/chain mapping). It does not name explicit alternative tool IDs like chain_company_num or chain_have_no_patent_company_num, but the exclusions and examples make it clear when this tool applies versus when another patent-status or list variant would be appropriate.

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