ABS Job Vacancies (JV) — total vacancies (thousands), All sectors / All industries, Original estimates, quarterly. 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?
With no annotations, the description carries the transparency burden. It discloses the data source, estimation type, frequency, and default region behavior. It does not explicitly say the operation is read-only, but the nature of a data-query tool makes that inherent. The disclosed details add meaningful context beyond a simple name.
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, dense sentence with no filler. It front-loads the dataset name and packs in units, scope, frequency, and default behavior efficiently.
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 low-complexity tool with one optional parameter and no output schema, the description covers the essential facts: data source, metric, units, scope, frequency, and default region. It does not enumerate valid region values, but the schema's parameter description partially addresses that. Overall, it is complete enough for correct invocation.
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% for the single region parameter, so the schema already documents it. The description reinforces the default ('Region defaults to National') but adds no new parameter-level semantics beyond what the schema states.
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 states a specific dataset (ABS Job Vacancies) with detailed attributes: total vacancies in thousands, all sectors/industries, original estimates, quarterly. This clearly distinguishes it from sibling tools like au-unemployment or au-cpi without ambiguity.
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 gives clear context about the data (quarterly, national default) but does not explicitly state when to use this tool versus alternatives. Since sibling tools cover different economic indicators, the intended use is implied rather than spelled out.
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.