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

With only openWorldHint in annotations, the description carries the transparency burden. It discloses the merged return format (total/production/sales/dependent type text), the scope constraints (specific region granularity and chain name), and what is excluded. This adds meaningful behavioral context beyond the minimal annotations. There is no contradiction with openWorldHint since the tool accepts open-ended strings for region/chain.

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 with clear sections: main capability, indicator types, exclusions, and typical questions. The example questions are highly valuable for an agent to pattern-match. Minor redundancy exists where the '涉及指标/类型' section partially repeats the return format already stated in the first sentence, but overall it remains compact and focused.

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 the tool has an output schema (so return values need not be described), 100% schema coverage, and a moderate complexity (single count query with type breakdown), the description covers the purpose, return format, exclusions, and usage examples. The pricing block adds operational context. This is reasonably complete for a count-type tool, slightly shy of exceptional due to not addressing error conditions or edge 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?

Schema description coverage is 100%, so the baseline is 3 per the rubric. All three parameters (year, region, chain_name) are documented in the schema with examples. The description adds marginal value by clarifying region granularity levels (country/province/city/district) and the merged return types, but this is incremental rather than substantial beyond what the schema already conveys.

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 identifies the verb (query/count), the resource (specialized/new enterprises - 专精特新企业), and the dual-faceted scope (specific region + industry chain name). It distinguishes itself from sibling tools like chain_specialized_company_list (list variant) and other chain_*_num count tools by specifying the exact return (merged total/production/sales/dependent type counts). The granularity of regions (country/province/city/district) is explicitly stated.

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 '不包含' (does not include) section provides explicit exclusions: other company category statistics and company list details, which implicitly directs users to the list tool (chain_specialized_company_list) and other count tools. The three typical-question examples provide concrete usage contexts. However, it does not explicitly name the alternative tools, so it stops short of the 5-level where alternatives are named directly.

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