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

With no annotations, the description carries the burden of behavior disclosure. It reveals the key rule and outcome (whether the month's premium is owed), which is behaviorally important, but it does not explain the return format, whether both employee and employer premiums are addressed, or edge-case handling such as mid-month entry or missing dates. A 3 is fair because the core deterministic rule is transparent, yet implementation details remain opaque.

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 heavily front-loaded with a compelling warning and a concrete example, then ends with the directive. Every sentence serves a purpose, though the opening 'single most expensive month-end mistake' framing is somewhat repetitive of the title's subject. It is not bloated and reads quickly.

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 judgment tool with three self-explanatory parameters and no output schema, the description explains the core rule, gives a concrete edge case, and tells the assistant to always delegate to it. It leaves out the exact return value shape, but the absence of an output schema and the simple yes/no nature of the judgment make this a minor gap rather than a serious one.

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%, so the schema already documents all three parameters well. The description adds emphasis that left_on is the last day actually worked, not the day after, which is a valuable semantic correction beyond the schema. However, it does not clarify the interplay between joined_on and left_on, such as when both are supplied for someone joining and leaving in the same month, so it only modestly exceeds the schema baseline.

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 this tool judges insurance premium liability for joining/leaving months in Japanese payroll, and the unusual rule is spelled out. Its title '入社月・退社月の保険料の要否' plus the description of 'Coverage ends the day AFTER the last day worked' makes the purpose unmistakable and distinct from sibling tools that calculate amounts or rates.

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?

It explicitly tells the assistant this is the authoritative check for the single most expensive month-end mistake and says 'Always check here rather than reasoning it out.' This both establishes when to use it (whenever judging premium liability for months around joining/leaving) and effectively excludes guessing/memory or alternative calculation tools.

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