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

The description discloses that the tool returns aggregated counts as text and does not include company lists or other classifications. It also mentions the requirement for a specific region and chain name. There is no contradiction with the openWorldHint annotation, and it provides sufficient context for a read-only aggregation query.

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 moderately sized, with a clear structure: introduction, included metrics, exclusions, and typical examples. It is efficient and front-loaded, though it includes pricing information that might be redundant or belong elsewhere, but does not detract heavily.

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's moderate complexity (three parameters, output schema exists) and full schema coverage, the description is complete enough. It specifies the return type (aggregated text), examples, and exclusions. The output schema is not available but the description covers key aspects, making it sufficient for the agent.

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 schema covers 100% of parameters with descriptions, but the description adds value by clarifying that 'region' can be a national, provincial, city, or district-level name (examples like '全国', '成都'), and that 'chain_name' refers to industry chain or node names (e.g., '集成电路'). It also mentions 'year' as statistical year, though not detailed in the description but in schema.

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 the tool's function: to query the count of A-level taxpayer companies based on a specific region and industrial chain, returning a combined text of totals and types (production, sales, dependency). It includes specific metrics and typical examples, and distinguishes from sibling tools like chain_a_taxpayer_company_list by focusing on counts rather than lists.

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 strong usage guidance by listing what is included and excluded (e.g., excludes other enterprise classifications and company name details), which helps the agent decide when to use this tool. However, it does not explicitly mention alternatives or when not to use it, though the exclusion of company list details implies the list counterpart for detailed lists.

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