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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety and determinism are covered structurally. The description adds genuinely useful context beyond that: results are computed from live listings (data recency/source) and output is CC BY 4.0 licensed (attribution obligation). It does not disclose whether stats are cached, how listing recency is bounded, or sample-size caveats.

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 dense sentence that front-loads the subject (English-teacher salary statistics for South Korea) and then lists covered dimensions without filler. The trailing license fragment is slightly bolted on but still earns its place for attribution compliance.

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 input schema fields and no output schema, the description carries the burden of describing what comes back, and it does so by enumerating the statistic families produced plus the data source and license. An agent can decide relevance and call it with no arguments; only the exact response shape and any caveats on sample size remain unspecified.

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, which is the baseline-4 case per the rubric. The description instead specifies the output dimensions (median, quartiles, region, employer type, hourly pay, disclosure, housing), which adds framing value even though there is nothing to disambiguate on the input side.

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 resource and scope: English-teacher salary statistics for South Korea computed from live listings, and enumerates the exact metrics produced (median, quartiles, by region/employer type, effective hourly pay, disclosure rates, housing). That is far more specific than a tautology, though it never explicitly positions itself against siblings like market_stats or disclosure_stats, so an agent must infer the boundary from scope alone.

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 explicit when-to-use statement or named alternative, but the enumerated metric list strongly implies the question types this tool answers (regional pay comparisons, hourly-rate normalization, housing). Usage is inferable rather than stated, and the overlap in scope with market_stats/disclosure_stats is left unresolved.

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