get_minimum_wage

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

Annotations already establish readOnly, idempotent, non-destructive behavior. The description adds valuable context beyond that: the data source (MHLW), the annual October revision, the returned fields (latest rate, previous rate, increase, effective date), and the warning that LLM memory is stale. This is meaningful behavioral disclosure.

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?

Two focused sentences: the first states the core purpose and scope, the second adds official source, revision timing, return contents, and the stale-memory warning. Every sentence earns its place with no filler.

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?

With one optional parameter, complete schema coverage, rich annotations, and an output schema present, the description covers everything else an agent needs: what the tool returns, when data updates, and why the tool should be trusted over the model's own knowledge.

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 only parameter, 'prefecture', has 100% schema description coverage with format examples, default, and the '全国/nationwide' special case. The tool description itself adds little parameter-specific meaning, but the schema already carries the full burden, so the baseline of 3 applies.

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 states a specific verb ('Get'), a specific resource ('regional minimum wage for a Japanese prefecture'), and clarifies it returns the hourly rate. It also distinguishes itself from sibling tools by emphasizing 'current' figures versus history, ranking, or prefecture listing.

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 for when to use this tool: when you need the current official minimum wage for a prefecture, explicitly noting the October revision cycle and that LLM memory is stale by design. It does not explicitly say 'use get_minimum_wage_history for historical data' or 'use rank_minimum_wages for comparisons,' so exclusions are only implied.

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