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

With readOnlyHint/idempotentHint already covering the safety profile, the description adds substantive context: all values are in THOUSANDS, sector detail sums exactly to the headline, percentage-type figures live under `standing` rather than the measure axis, and the data is tier-agnostic published statistics. That is real payload semantics an agent could not infer 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?

Scope is front-loaded in the opening clause, then return format, then filtering behaviour — a sensible order with no filler. The first sentence is a long enumeration, but each item adds discriminating detail rather than padding.

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?

There is no output schema, so the description must carry return semantics, and it largely does: measure axes, month-over-month change plus 3/6/12-month windows, thousands as the unit, and where percentages land. It could say more about how sector attribution is keyed in the response, but nothing essential for correct invocation is missing.

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 both `measures` and `history` are already documented in the schema, and the description mostly restates them ('measures filters by key, history adds the monthly series'). `window` and `fresh` are left entirely to their schema descriptions. Baseline 3 is appropriate when the schema carries the parameter burden.

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 names a concrete resource scope — every US employment measure (nonfarm payrolls split private/government, ten private sectors, claims, JOLTS) — and states the return shape (monthly change plus 3/6/12-month windows in thousands). An agent can immediately tell this apart from siblings like get_inflation, get_earnings or get_economic_calendar.

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?

The domain boundary is explicit enough to route correctly: it covers US employment/claims/JOLTS only, and 'Published statistics, so it answers on every tier' tells the agent there are no entitlement preconditions for the base data. It stops short of naming a sibling alternative or a when-not-to-use condition, so it is clear context without exclusions.

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