Public school term dates for each Australian state/territory (4 terms per year). For parents, travel, and childcare planning agents.
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | Calendar year (default: 2026) | |
| state | Yes | NSW, VIC, QLD, SA, WA, TAS, NT, or ACT |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the burden. It discloses data content and term count but not how results are returned (e.g., term start/end dates, JSON shape) or whether output covers future years beyond the default. The 'public school' qualifier and 4-terms-per-year detail add some useful context.
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?
Two short sentences front-load the core purpose and then add the intended audience. No filler or redundant restatement of schema fields.
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 simple data-lookup tool with fully documented parameters, this is mostly complete, but there is no output schema and the description does not specify the return format or that it provides start/end dates. An agent would need to infer the exact response structure from the tool name and schema alone.
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%, so the description is not required to document parameters. The narrative adds no new semantics beyond the schema's 'year default 2026' and state abbreviations, so 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 clear resource ('public school term dates') and scope ('each Australian state/territory'), and '4 terms per year' clarifies granularity. It does not use an explicit retrieve/provide verb, but the meaning is unambiguous and the Australian scope distinguishes it from nz-school-terms.
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 second sentence names concrete use cases (parents, travel, childcare planning), giving clear context for when to invoke. It does not explicitly state when not to use it or mention alternatives such as au-public-holidays or nz-school-terms, though the scope makes the main distinction implicit.
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.