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

With only openWorldHint as annotation, the description adds valuable context by stating the exact count types returned, the merged text nature of the return, and exclusions. It does not contradict the annotation or overstate its behavior; minor gaps remain around edge cases like zero-result handling, but this is adequate for a numeric query 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 description is compact and front-loaded with the core query purpose, followed by metrics, exclusions, and examples. It contains slight redundancy between the initial '总量/生产型/销售型/依赖型' list and the later repeated metric enumeration, but overall nothing is wasted.

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 relatively simple aggregation tool with full schema description coverage and an output schema, the description covers scope, supported metrics, exclusions, and realistic query formulations. The phrase about 'merged text' is slightly ambiguous but not enough to significantly impair selection or invocation.

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 already covers all parameters with descriptions, so the bar is moderate. The description adds useful semantic detail by enumerating region levels (country, province, city, district) and providing concrete examples for region, chain_name, and year, which helps correct parameter formatting.

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 tool's action: querying A-level taxpayer enterprise counts by specific region and industry chain, with explicit metric breakdowns (total/production/sales/dependent). It also distinguishes itself from list-type siblings by stating that company list details are not included.

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 typical question phrasings and explicitly states what is excluded (other classifications and company list details), which helps the agent decide when to use this tool. However, it does not explicitly name chain_a_taxpayer_company_list as the alternative for list-detail use cases.

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