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

No annotations are provided, so the description carries the behavioral disclosure burden. It usefully states the data source, national store coverage, item count, monthly frequency, and base period. It does not describe exact output columns or update lag, but for a no-parameter dataset this is reasonable.

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?

One dense, front-loaded Japanese sentence conveys the topic, source, store count, item count, base period, and examples with no filler or redundancy. Every element earns its place.

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 tool with no output schema, the description provides sufficient context: source, scope, metric definition, and representative items. There is no parameter risk, and the only minor gap is lack of detail on the returned result structure.

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 and the schema is trivially fully covered, so there is no parameter-level meaning for the description to add. Per calibration, zero parameters earns a baseline of 4.

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 identifies the resource (supermarket prices), the specific data source (Consumer Affairs Agency POS, about 1,200 stores), the scope (26 items), and the metric (monthly index, Reiwa 7 average = 100). This clearly distinguishes it from data-lookup siblings like super_item or prices_now, despite not using an explicit verb.

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 implies usage: an agent can infer it should be selected when supermarket price-index data is needed. However, it gives no explicit guidance about when to prefer this tool over siblings, nor does it mention excluded cases or alternative tools.

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