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

Beyond the readOnly/destructive annotations it discloses what the output actually contains (salary basis, per-line rate, base, floor/ceiling, source and check date, monthly/annual toggle, recalculation field, CSV copy) and the data snapshot date 2026-10-22, plus an explicit non-advice disclaimer. This is substantive behavioral context an agent cannot get from the annotations.

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?

Front-loaded with the use case, then invocation rule, then output contents — a sensible order. It is dense with stacked parentheticals and example phrases, so it runs long, but almost every clause carries information.

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 structure need not be explained, yet the description still tells the agent what will be rendered and that a CSV copy is available. Combined with the snapshot date and the 'computes figures itself' note, an agent has everything needed to call it correctly on the first try.

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% and the schema already documents salary, country, assumptions, salary_period, salary_currency and payments_per_year including the monthly/statutory-payments interaction. The description's own parameter contribution is mostly the cross-tool rule that it accepts the same country/salary/assumptions as employer_cost, which is useful but marginal. Baseline 3 is appropriate.

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 verb-and-resource: display the line-by-line employer cost of one employee in a country as a table or card. The split from employer_cost is explicit — that tool computes, this one renders the figures and computes them itself — so an agent can distinguish it from siblings without reading schemas.

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?

Gives explicit when-to-use triggers ('show me the breakdown', 'display it as a table', plus a non-English example) and an explicit invocation rule: call directly with no data tool first, or after employer_cost passing through the `widget` arguments. Alternatives and ordering are both spelled out.

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