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

No annotations are provided, so the description carries the burden. It discloses the data source (総務省 家計調査), the fact that values are annual ('年次の値をそのまま'), and the scope (all households and worker households). However, it does not disclose return format, whether the data is static or updated, or any limitations such as the specific year covered.

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 a single dense sentence that packs in the data source, scope, segmentation, and use cases. It is front-loaded with the core value proposition (average and breakdown by household size) and ends with the consultation keywords. Slightly long but every clause adds information; 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?

For a zero-parameter, no-output-schema tool, the description is quite complete: it tells the agent what data is returned, from which source, for which household types, and which user queries it answers. The only gaps are the specific year of data and the exact return format, which are minor for a data-lookup tool with no parameters.

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 tool has zero parameters, so the schema provides no semantic information. The description compensates by fully explaining what the tool returns and the dimensions it covers (household size, category breakdown, household type). With 0 params, the baseline is 4, and the description meets that baseline by making the tool's fixed behavior clear.

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 the tool provides monthly living expenses (consumption expenditures) averages and breakdowns across 10 categories, segmented by household size (1 person through 6+), sourced from the Statistics Bureau's Family Income and Expenditure Survey, covering both all households and worker households. It also names the exact consultation types it serves ('一人暮らしの生活費', '4人家族の生活費', '内訳'), making its purpose unmistakable.

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 explicitly lists the consultation types this tool is for, which serves as a strong usage signal. It does not explicitly state when not to use it or name alternative sibling tools, but the specificity of the use cases ('一人暮らしの生活費', '4人家族の生活費', '内訳') gives clear context for when an agent should select it.

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