Australian public holidays for all states/territories: full list for a year plus the next upcoming holiday with days-away. Ideal for scheduling, payroll, travel, and long-weekend questions.
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | Calendar year (default: current year) | |
| state | No | NSW, VIC, QLD, SA, WA, TAS, NT, ACT, or ALL (default) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It does a good job by stating the output includes a full yearly list and the next upcoming holiday with days-away, information not present in the schema. It does not mention response formatting or data source details, but the core behavior is transparent.
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 concise sentences with no filler. The first sentence states the resource and output content, the second gives use cases. Every phrase earns its place, and the most important information is front-loaded.
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?
This is a simple two-optional-parameter tool with no output schema and no annotations. The description explains the main return values, covers the geographic scope, and mentions relevant use cases. It could be more explicit about how the state parameter filters results, but the schema covers that, so overall the description is sufficiently complete for an agent to invoke it correctly.
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 the baseline is 3. The description mentions 'year' and 'all states/territories' but does not add meaning beyond what the input schema already documents for the two parameters. It does not introduce any new parameter details or format constraints.
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 clearly identifies the resource (Australian public holidays) and what it returns: a full yearly list plus the next upcoming holiday with days-away. It lacks an explicit verb like 'get' or 'list', but its meaning is unambiguous and it distinguishes itself from siblings such as nz-public-holidays and au-school-terms by the Australian holiday focus.
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 concrete use cases ('scheduling, payroll, travel, and long-weekend questions'), which tells an agent when this tool is relevant. It does not explicitly state when not to use it or name alternative tools, but the context is clear enough for selection among the provided siblings.
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.