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

With only openWorldHint among its annotations, the description carries significant transparency burden and delivers: it discloses that results are always constrained to a two-year window, that the return merges four metric categories (总量/生产型/销售型/依赖型), and what metric types are not included. It doesn't delve into the implications of openWorldHint on data coverage, but the provided behavioral insight is valuable and doesn't contradict the annotation.

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 earns its space with a clear visual hierarchy: a one-sentence definition, a compact metric list, an exclusion line, typical questions, and pricing metadata. The pricing JSON is somewhat verbose for an unstructured description field, but it's clearly delimited and the entire package remains scannable with several meaningful sections.

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 tool with only 3 parameters, an existing output schema, and 100% schema parameter coverage, the description covers use cases, exclusions, examples, and scenarios comprehensively. Minor gaps remain, such as how the optional `year` parameter interacts with the fixed 'past two years' window, and a clearer note on the openWorldHint implications would further strengthen it, but overall it's quite complete.

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 coverage is at 100% with rich examples for every parameter (e.g., 「全国」「北京市海淀区」for region, 「集成电路」for chain_name). The description adds realistic combinations through the typical questions, but the core semantics of each parameter are already well-established by the schema examples and descriptions. It provides modest incremental value at best.

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 uses a specific verb+object structure (近两年发起过招标的企业数量查询) and clearly defines the geographic scope (国家，省份，城市，区县), time window (近两年), and aggregation basis (具体产业链名称). It unambiguously communicates what the tool does while disambiguating from sibling list tools via the explicit exclusion (不包含...企业名单明细) and the fact that it emphasizes count-only results.

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 exclusions (不包含：其他企业分类的统计；企业名单明细) and three realistic example queries (典型问法) that show typical invocation patterns from different angles. However, it never explicitly points to a sibling like chain_issued_tender_company_list as the alternative when name-level detail is needed, leaving the agent to infer from naming conventions.

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