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

Annotations already declare readOnly/openWorld/destructive=false, so the description is free to add higher-value semantics: it discloses that a sample size accompanies each figure and that currencies and pay periods are reported separately and never converted. That last caveat is essential for correct interpretation and is not derivable from annotations or schema. It stops short of documenting coverage thresholds or how small samples are handled.

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?

Two sentences, no filler, and the most important information (what the numbers are and that they come only from postings stating pay) is front-loaded ahead of the interpretation caveat.

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?

An output schema exists, so return values need no explanation, and the description covers source, scope, and the currency/sample-size caveats an agent needs to read results correctly. The one gap is that all three parameters are optional with no stated default or behavior for an unfiltered call, which matters given zero required parameters.

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 every parameter (country, category, seniority) is already documented with format and example values in the schema. The description restates the same scoping axes in prose without adding format, default, or combination rules, so the 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?

States a specific verb (report percentiles) plus the exact statistics returned (25th, median, 75th), the data source (open postings that state pay), and the scoping dimensions (role family, seniority, country). An agent can distinguish this aggregate-pay tool from job_market_stats or search_jobs without opening any schema.

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 rather than stated: the phrase 'from open postings that state pay' quietly warns that the sample is incomplete and excludes postings without salary data. No alternative sibling is named and no condition selects this tool over job_market_stats, so the routing decision is left to inference.

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