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

The description does not explicitly state behavioral traits such as read-only nature, return format, or pagination. Annotations only include openWorldHint, which doesn't convey operational behavior. The description implies a query operation but lacks details on output structure or any side effects, leaving some ambiguity.

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 for purpose, exclusions, and example queries. It is concise enough to convey the necessary information without excessive verbosity, though it does repeat some content between the main description and the indicators/exclusions 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?

The description covers the tool's core function, its exclusions, and typical usage examples. It adequately prepares an agent to decide when to use this tool. However, it does not mention the nature of the output (e.g., boolean, list) or error handling, which could be relevant for integration, but overall it is sufficiently complete for its intended use.

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 provides descriptions for company_name and country_name with examples. The description adds typical question examples but does not provide additional semantic detail beyond those examples. Since schema coverage is 100%, the description adds minimal extra value for parameter understanding.

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 specifies the tool's function: querying executive change information (core management changes, executive departures, and transfers) for a given company. It also explicitly states what it is not for (negative sentiment or scandal queries, batch screening by park/industry chain), distinguishing it from sibling tools like enterprise_change_executive_sentiment and list-based tools.

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 usage examples (e.g., 'China BYD Co., Ltd. core management changes?') and clarifies exclusions (not for negative sentiment, not for batch screening). This gives practical guidance on when to use this tool, though it could be more explicit about when to prefer this over closely related tools like enterprise_change_key_roles.

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