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

Annotations only include openWorldHint, which is not a safety indicator. The description does not state that the tool is read-only, does not describe return behavior, pagination, or any side effects. It does clarify scope and exclusions, but for a query tool with minimal annotations, more transparency about expected behavior (e.g., read-only nature) is needed.

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 clear sections (included metrics, exclusions, typical questions) and is front-loaded with the main purpose. However, it includes pricing information that is not directly relevant to function selection, making it slightly longer than necessary. Still, it is organized and scannable.

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 provides a solid overview: what the tool does, what it doesn't include, and example queries. Since an output schema exists, return format is covered outside the description. The description is sufficiently complete for a single-company query tool, though it could mention error handling or edge cases (e.g., no data found).

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% (both parameters have descriptions), so the baseline is 3. The description adds value by providing concrete examples (e.g., '比亚迪股份有限公司', '中国') and clarifying that the tool is for per-company queries, not batch screening. This enhances understanding beyond the schema descriptions.

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 it queries periodic changes in legal responsibility aspects for a specific company, specifically litigation counts, win rates, and IP, personal information, and consumer protection lawsuits. It explicitly lists included metrics and excludes unrelated categories, distinguishing it from sibling tools like enterprise_change_legal_compliance_risk and enterprise_change_violation_illegal.

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 clear context by stating what it is not for (e.g., discredited/restricted high consumption, business anomalies) and gives example queries (e.g., '中国比亚迪股份有限公司诉讼案件数量'). However, it does not explicitly name alternative tools for the excluded cases, so it lacks explicit alternatives but has strong when-not guidance.

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