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

Annotations only include openWorldHint: true. The description adds significant behavioral context: it indicates a query/read nature (using '查询'), enumerates exactly which indicators are covered, and enumerates exclusions. It does not mention any destructive or write actions, so transparency is high. Minor omission: no explicit statement about side-effect-free or data freshness, but the query intent is clear.

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 structured with a clear purpose statement, a list of included indicators, a list of exclusions, and three examples. It is front-loaded with the main purpose, every sentence earns its place, and it avoids unnecessary fluff despite its length. The inclusion of pricing is a separate block but not verbose.

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 specificity and the large number of sibling tools (many enterprise_change_*), the description fully covers scope, exclusions, and use cases. It explains exactly what indicators are returned, what is not covered, and gives examples. The presence of an output schema means return-value details are not needed, and the description is sufficient for an agent to select and invoke correctly.

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 100% with examples for each parameter. The description adds value through typical query examples (e.g., '中国比亚迪股份有限公司合同规范性') that illustrate how to combine country_name and company_name, reinforcing the parameter usage in context. It does not redefine schemas but complements them with usage patterns.

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 queries periodic changes in cooperation brand aspects for a specific enterprise, listing concrete indicators like contract compliance, delivery timeliness, supply qualification rate, price rationality, service satisfaction, and complaint rate. It specifically distinguishes from siblings by excluding outcome metrics (e.g., accounts payable, default rates) and batch screening, and provides typical query examples, making purpose unambiguous.

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?

Explicitly states when to use the tool (for process-related cooperation metrics) and what it is not for (outcome indicators, non-category indicators, batch screening). Offers typical query patterns with country-company-metric combinations, giving clear guidance on invocation and exclusions.

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