ABS Average Weekly Earnings (AWE) full-time adult ordinary-time earnings, Persons, All industries. Semi-annual. Region defaults to National.
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | ABS region or "National" (default) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry behavioral disclosure. It adds useful context: the series is semi-annual and defaults to National. However, it does not mention whether the tool returns a time series, the latest value, or any other output characteristics, leaving significant behavioral gaps.
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?
The description is a single compact sentence that conveys the data identity, frequency, and default region without fluff. It is not front-loaded with an action verb, but every word 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?
For a one-parameter lookup with no output schema and no annotations, the description covers the data identity and basic scope but leaves the response format and whether it returns a single value or a series unspecified. An agent may be uncertain about what to expect.
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% for the single region parameter, and the schema already states it is an 'ABS region or "National" (default)'. The description only reaffirms the default, adding minimal new meaning beyond the schema.
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 identifies a specific ABS statistical series (Average Weekly Earnings, full-time adult ordinary-time earnings, Persons, All industries) with enough precision to distinguish it from sibling tools like au-income or au-cpi. It lacks an explicit verb like 'get' or 'returns', but the resource and scope are unambiguous.
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 description implies usage by naming the exact data series, but it does not explicitly say when to use this tool versus alternatives such as au-income. The 'Semi-annual' and 'Region defaults to National' hints provide some context, but no routing guidance or exclusions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Every tool maps to a clearly distinct dataset or lookup, with country prefixes and topic names separating overlapping domains. Even similar tools like au-abs-building-activity and au-abs-building-approvals are unambiguously differentiated by their descriptions.
The data tools follow a consistent country/topic hyphenated pattern (au-*, nz-*), making resource selection predictable. The meta tools (get_catalog, list_services, health) break this pattern with imperative/underscore names, but this is a minor and understandable deviation.
At 26 tools, the set is on the heavy side and slightly exceeds the typical comfortable range. However, each tool represents a genuinely distinct data service, and the clear grouping by country and topic keeps the surface navigable.
The server covers a broad range of common agent data needs for Australia and New Zealand: demographics, income, building, labour, weather, time, holidays, school terms, and place resolution. Minor gaps exist, such as no NZ building data or broader international coverage, but core workflows are well supported.