databutler-jp-living-costs

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

With readOnlyHint/idempotentHint declared, the safety profile is already covered, yet the description still adds substantial context: each figure carries value, unit, Japanese term, quoted source row, source URL, effectiveFrom/effectiveTo, and the answer carries verifiedDate, freshness and a citation permalink. Since there is no output schema, this disclosure is doing real work in telling the agent what will come back and how to interpret it.

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 entire definition is one semicolon-chained sentence of roughly 250 words, and much of it restates data the tool will return anyway (effectiveFrom windows, FY2025 previous values, exact yen/rate units). The purpose is front-loaded, but the lack of any structural break makes the category list hard to scan and pushes well past what is needed to call the tool.

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 single optional enum parameter with no output schema, the description covers the value space, the per-category contents, the returned fields, and the unknown/omitted-category behavior. Nothing an agent needs in order to select and invoke the tool correctly is missing.

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% (the enum list of categories is already in the schema), so the baseline is 3, but the description adds genuine meaning per category — e.g. that minimum-wage covers 47 prefectures keyed by romanised names like 'tokyo'/'osaka', with national weighted average and FY2025 previous amounts. That detail helps an agent predict the payload shape rather than just the accepted value.

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 opening clause names the resource precisely ('Japan daily-life official figures, verified against the official pages') and enumerates the nine categories returned, so an agent knows exactly what it retrieves. It is not differentiated from a sibling because none exists; the only ambiguity is that the front-load is buried inside a very long sentence.

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 states the two edge-case behaviors an agent needs: 'No category returns the listing' and 'an unknown category answers covered:false with the nearest keys', which tells the caller how to recover. With no sibling tools, there is no alternative-selection guidance to give, so this is close to complete for the tool's role.

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