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

With no annotations provided, the description carries the full disclosure burden. It goes beyond a simple statement by defining what '最安値' means (lowest across all Rakuten stores), and specifies the returned attributes: number of stores, reviews, and annual power consumption. It does not describe freshness windows or failure behavior, but for a straightforward lookup tool the disclosed scope is reasonably transparent.

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 compact and front-loads the core value proposition. Every clause contributes: the data type, the exact meaning of 'lowest price', the supported categories, and the intended user queries. No filler is present.

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 tool with one parameter and no output schema, the description is largely complete. It explains what data is returned and which categories are supported, and it ties the tool to realistic user requests. Minor gaps remain around output formatting and handling of invalid model numbers, but these are not critical for a low-complexity lookup tool.

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 0%, so the description must compensate. It explains the 'cat' parameter by listing many valid values (tv, fridge, aircon, washer, microwave, rice-cooker, vacuum, dryer, air-purifier) and clarifies that these are appliance categories. It could be more precise about the complete allowed set, but the examples and 'など' provide enough orientation.

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 provides today's lowest price for home appliances by model number, including specific data points like store count, reviews, and annual power consumption. It also gives concrete example queries, which differentiates it from sibling tools by establishing the model-number-specific scope.

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 gives usage context with example user intents such as '<型番> 最安値' and 'この機種はいくら', making it clear when this tool is appropriate. It does not explicitly mention when not to use it or name alternative sibling tools, so it stops short of full exclusion guidance.

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