salary_stats

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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds genuinely useful behavioral caveats beyond that: figures are computed only from postings that state pay, a sample size accompanies each figure, and currencies/pay periods are reported separately and never converted — real constraints an agent needs to interpret results.

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 dense sentences, no filler, and the primary output (percentiles), the source constraint, and the currency caveat are all front-loaded before any detail.

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 does the heavy lifting by naming the returned measures, the sample-size field, and the per-currency/per-period reporting. It is nearly complete, but omits how results are grouped when parameters are omitted and whether multiple currencies produce multiple rows.

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 coverage is 100%, so country/category/seniority are already documented with examples and allowed values. The description restates that these scope the result (and hints at role-family/seniority semantics) but adds no syntax, defaults, or behavior for omitted parameters, so it sits at the baseline for high-coverage schemas.

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 concrete resource (salary percentiles at 25th/median/75th) and the exact dimensions it is scoped by (role family, seniority, country), plus a non-obvious data source constraint (only postings that state pay). It does not, however, distinguish itself from nearby siblings like job_market_stats or market_fit, leaving the agent to infer which stats tool to pick.

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 scoping sentence — call it when you want pay distribution for a role/seniority/country slice — but there is no explicit 'use this when… / use X instead' guidance and no mention that the three parameters are all optional, so the agent cannot tell how to get an unfiltered aggregate or when job_market_stats is the better call.

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