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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is fully covered. The description adds real value on top by disclosing the breadth of the computed output (YoY, 3-year change, growing/declining partners, concentration), which tells the agent this is an expensive aggregated read rather than a raw lookup. It does not mention response size limits or the currency/unit of the values, so it stops short of 5.

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 English half is well front-loaded: the core purpose and the 'use first' directive are stated before the content list. The structure costs points because the entire text is duplicated in Japanese, roughly halving information density, and the long mid-sentence inventory of outputs could be tightened.

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?

With no output schema, the description carries the burden of describing return values, and it does so thoroughly by naming every component of the payload. The remaining gap is that it never states the unit/currency of the monetary values or the approximate shape of the response (e.g., whether partners are ranked or capped), which matters for a multi-metric aggregation 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 description coverage is 100% and three of four parameters carry enums with descriptions, so the schema does the heavy lifting. The description adds nothing about parameter behavior — notably it does not clarify how `level` interacts with the code length inference or what changing `lang` affects beyond the schema's own note. Baseline 3 applies.

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 opens with a specific verb+resource framing ("One call for an item overview") and then enumerates the exact contents returned: 12-month value with YoY and 3-year change, partners and their growth, new markets, seasonality, yearly trend, concentration, and a one-line summary. This enumeration clearly distinguishes it from the narrower siblings (get_partners, get_seasonality, get_series) by positioning it as the aggregated overview.

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 gives an explicit routing rule twice ("Use this first for questions about an item" / "品目について聞かれたら、まずこれを使います"), which is strong first-choice guidance. However, it never states when NOT to use it — e.g., that get_series should be used for full time series or get_partners for partner-level depth — so the agent must infer the boundary from the sibling names.

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