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 implies a read-only statistical lookup, but says nothing about the shape of the result, whether it is aggregated or per-category, or what the '比率' baseline is. Only the data source (Insee 9 categories) is disclosed.

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?

A single short sentence with no filler, and the data source is front-loaded. Its brevity is close to under-specification rather than padding, but nothing is wasted.

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 lookup this covers the data source and the comparison concept, which is the minimum an agent needs. Without an output schema, though, it should say more about what the ratios look like and how the result differs from compare_wages.

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 takes zero parameters, so there is nothing for the description to compensate for; baseline 4 applies. The mention of the fixed 9 Insee categories usefully explains why no filtering input exists.

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 names the resource (French Insee 9 occupational categories mapped to Japanese equivalents) and the operation (comparing hourly-wage multipliers), so an agent can roughly tell what it returns. However the phrasing is grammatically garbled ('の比べ') and it never distinguishes itself from the sibling compare_wages, which sounds like it does the same comparison.

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?

There is no indication of when to prefer this over compare_wages or wages_ranking, nor any prerequisites or exclusions. The agent must infer usage purely from the data-source mention.

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