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

Annotations already establish the safe-read profile (readOnly, idempotent, closed-world), so the bar is lower, yet the description still adds real context: data is 'live', counts are of adverts rather than employers, and the median salary is only reported 'where the sample allows'. That last point usefully warns about missing values.

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 front-loads the scope (South Korea, by region) and then enumerates the metrics, closing with the licence. Efficient, though the mid-sentence clause stack is slightly heavy.

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 parameters and no output schema, the description carries the burden of describing what comes back, and it does so by naming the four reported measures plus the advert-vs-employer caveat. A note on granularity (e.g. region unit) or sample-size threshold would make it fully complete.

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 per the rubric the baseline is 4. There is nothing for the description to clarify beyond confirming the tool is parameterless and returns a fixed aggregate.

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?

States a precise resource (live English-teaching job adverts in South Korea) and the exact aggregation (counts by region, direct-hire vs recruiter split, on-site hours, median advertised salary). It is clearly distinguishable from siblings like salary_stats or disclosure_stats by naming the region-and-metric 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?

Usage is implied by the aggregation framing, but there is no explicit when-to-use or when-not-to-use guidance, and no sibling (e.g. salary_stats, disclosure_stats) is named as the alternative for a different question. An agent must infer that this is the region-breakdown aggregate.

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