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

No annotations are provided, so the description carries the full burden. It discloses that the tool computes from government standards, uses Rakuten best-seller data, and returns median prices and rotation cycles. It also implies the tool is read-only and calculation-based. It doesn't mention failure modes, rate limits, or whether it makes external network calls, but for a calculation tool the disclosed behavior is reasonably complete.

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 paragraph that front-loads the core purpose and then lists inputs, outputs, and example queries. It is information-dense but not bloated. The example queries and screen path add value, though the sentence is long and could be slightly better structured with separators.

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 calculation tool with 3 simple parameters and no output schema, the description covers the inputs, the calculation basis, the outputs, and example usage. It doesn't describe the exact return format (e.g., JSON shape), but the output is a list of quantities and costs, which is reasonably inferable. The main gap is not specifying whether the result is a single number or a breakdown, but the description lists the breakdown items explicitly.

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 description coverage is 100%, so the schema already documents all three parameters. The description adds context by explaining how the parameters are used (adults vs kids, days 3/7/14) and the underlying assumptions (1 person 3L water/day, etc.), but it doesn't add new parameter-level details beyond the schema. Baseline 3 is appropriate.

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 ('返す' = returns) and a clear resource: household stockpile quantities and costs. It enumerates the exact inputs (adults, kids, days) and outputs (water, staple foods, main/side dishes, cassette gas, portable toilets, paper, batteries, total cost, rotation cycle). It also names the source standards (PMO, MAFF, METI) and example queries, which distinguishes it from siblings like 'kondate' or 'seikatsuhi'.

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 example user queries that should route to this tool ('備蓄は何日分', '水は何リットル', '4人家族の1週間分はいくら') and even states the screen path ('画面は /bichiku/'). This is strong usage guidance, though it doesn't explicitly name sibling alternatives to exclude. The example queries effectively serve as when-to-use signals.

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