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

With only openWorldHint: true in annotations, the description carries most transparency weight and delivers: it discloses that results are returned as merged text across four type dimensions (总量/生产型/销售型/依赖型) and clarifies the input region granularity. It adds exclusion context not inferable from the schema. However, it doesn't detail edge behavior such as what happens when a region/chain returns zero results or how open-world data gaps are handled, though the openWorldHint annotation partially softens this for a simple count tool.

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 content is logically organized into purpose, metrics/types, exclusions, and examples, with front-loaded intent. However, the embedded 'Pricing: {...}' block is extraneous metadata that clutters the description, and the metric list partially restates the title's 5-year concept. It earns its place but could be tightened—solid but not exemplary.

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 low-complexity tool (3 params, 2 required, no enums) with 100% schema coverage and an existing output schema (so return values needn't be re-explained), the description covers the key gaps: purpose, return-type composition, exclusions, and examples. The only minor omission is guidance on ambiguity (e.g., how region names are resolved), but this is a reasonable completeness level for this tool's simplicity.

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 params documented), setting a baseline of 3. The description adds value above this by elaborating the region param's granularity (国家/省份/城市/区县) and by showing through examples how chain_name and region combine (e.g., '2024年全国集成电路'). While useful, the incremental semantic depth over the schema is moderate, not extensive—hence a 4 rather than a 5.

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 a specific verb+resource: querying the count of enterprises surviving 5+ years for a specific region and industry chain, with merged returns for total/production/sales/dependent types. It explicitly lists included metrics and exclusions ('不包含：其他企业分类的统计；企业名单明细'), which differentiates it from sibling tools like chain_year5_company_list (list vs. count) and chain_company_num (without the 5-year filter). The typical-question examples further anchor its purpose.

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 usage context via typical question examples ('典型问法') covering different region granularities and chains, and specifies what is not included (other classification categories, company list details), which acts as a when-not-to-use signal. However, it never explicitly names alternative tools (e.g., chain_company_num without year5) or states the condition under which a different tool would be preferred, so it falls one step short of fully explicit alternatives.

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