Stats NZ median weekly earnings by region (Aotearoa Data Explorer LEED). 13 labour-graphing regions, latest Jan/June issue. Free API key required for the cron; request path is Redis-only.
| Name | Required | Description | Default |
|---|---|---|---|
| region | Yes | NZ region name, e.g. "Wellington" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description partially carries the behavioral burden by disclosing the 13-region scope, Jan/June issue recency, and the free API-key requirement. However, 'request path is Redis-only' is unexplained, and there is no mention of response shape, failure modes, or rate limits.
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 three short sentences with the purpose front-loaded and no redundant filler. The Redis-only phrase is cryptic but not wordy; the overall structure wastes no space.
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 tool, the source, measure, and recency are stated, but an agent still lacks the exact list of valid 13 regions and a clear explanation of how the API key and Redis-only request path affect invocation. The absence of an output schema makes the missing return-value detail more noticeable, though the purpose gives a good inference.
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?
The schema already documents the single 'region' parameter with a Wellington example, so the schema coverage is 100%. The description adds the 13-region scope but does not enumerate valid region values or clarify naming behavior beyond what the schema provides.
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 phrase 'Stats NZ median weekly earnings by region' states exactly what data the tool provides, and the Aotearoa Data Explorer LEED attribution distinguishes it from unrelated Australian siblings. It lacks an explicit verb and uses the cryptic 'labour-graphing regions' term, which prevents a perfect score.
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?
There is no when-to-use guidance or comparison to closely related siblings such as nz-demographics. The API-key and cron notes hint at an operational context but do not tell an agent when to choose this tool over alternatives.
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.