Jobs and unemployment

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

With no annotations, the description carries the full burden. It usefully discloses units (rates in %, counts in thousands) and the default adjustment basis, which matters for interpreting output. It does not describe the response shape, row/column layout, or any limits, so the behavioral picture is incomplete for a 6-parameter data tool.

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?

Three compact sentences, front-loaded with the dataset and scope, then units, then default basis. Almost every clause earns its place; 'and more' is the only vaguely padded phrase.

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?

For a 6-parameter tool with no output schema and no annotations, the description covers source, frequency, geography, units, and default adjustment, but omits any sense of the returned data structure and leaves sex and period parameters to the schema. Adequate but with visible gaps.

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 only 33%, so the description must compensate. It adds meaning for measures (rates vs counts), states ('Australia or any state/territory'), and adjustment (seasonally adjusted default and why). It says nothing about the sex parameter or the start/end period semantics, leaving part of the gap unfilled.

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 (get) plus a named dataset and its scope: ABS Labour Force survey, monthly, covering unemployment rate, participation, employment, for Australia or any state/territory. An agent can tell this apart from get_cpi or get_cash_rate by the dataset named, though no sibling is explicitly referenced.

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 dataset name rather than stated: nothing says when to prefer this over abs_get_data or rba_series, and there are no exclusions. The note that seasonally adjusted figures are 'what news reports quote' gives a mild steer toward the default, but no explicit when-to-use guidance.

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