Australian time zones per state: IANA zone, standard/DST offsets, current offset and local time, and whether daylight saving is active. Answers "what time is it in Perth" and "is it daylight saving" instantly.
| Name | Required | Description | Default |
|---|---|---|---|
| state | No | NSW, VIC, QLD, SA, WA, TAS, NT, or ACT (default: all states) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure, and it does a good job by enumerating the returned data: IANA zone, standard/DST offsets, current offset, local time, and DST status. It also signals a read-only informational behavior and fast lookup. It does not discuss edge cases or data sourcing, but for a simple lookup tool the main behavior is clearly conveyed.
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 compact: two sentences that front-load the core data elements and then give concrete example questions. Every sentence contributes useful information, and there is no redundant restatement of the tool name or schema details.
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 optional-parameter lookup tool, the description covers the essential behavioral contract: what data is returned and what questions it answers. Since there is no output schema, the explicit enumeration of output fields compensates well. It might be slightly improved by noting the output format or handling of invalid states, but those are minor gaps given the low complexity.
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 parameter's meaning is already documented in the input schema. The description adds context by mentioning states and the example "Perth", but it does not materially expand on the schema or clarify behavior for omitted versus provided state values beyond the schema's default note.
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 tool as providing Australian time zone information per state, listing specific output fields: IANA zone, standard/DST offsets, current offset, local time, and DST status. The examples, "what time is it in Perth" and "is it daylight saving", reinforce the concrete use cases and help distinguish it from sibling tools like nz-timezone.
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 when to use the tool—whenever Australian time zone or DST questions arise—and the examples make the intended queries explicit. However, it does not explicitly state when not to use it or mention alternatives such as nz-timezone for New Zealand inquiries, leaving the agent to infer the geographic boundary from the tool name and description.
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.