Latest observation (temp, rain, wind) + 7-day forecast for Australian suburbs via Open-Meteo. 1-day TTL.
| Name | Required | Description | Default |
|---|---|---|---|
| state | No | AU state code (NSW, VIC, etc.) | |
| suburb | Yes | Suburb name, e.g. "Parramatta" | |
| postcode | No | 4-digit postcode for tie-breaking |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral burden. It usefully discloses the data source (Open-Meteo) and the 1-day TTL, but it does not mention limitations, ambiguity handling beyond the schema's tie-breaking note, or output format details.
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?
A single, information-dense sentence that front-loads the core result, scope, source, and cache TTL. Every phrase adds value with no redundant or filler language.
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 straightforward lookup tool, the description covers the essential return contents, geographic scope, data source, and freshness. With no output schema or annotations, a bit more detail about units or response shape could help, but nothing critically needed for correct invocation is missing.
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%, and each parameter in the schema already has a meaningful description, including postcode's tie-breaking role. The tool description itself does not add much parameter-level meaning, so the baseline of 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?
The description clearly identifies the resource (Australian suburb weather) and the payload (latest observation plus 7-day forecast). It lacks an explicit action verb, but the intent is unmistakable and it is distinct from all sibling tools, none of which are weather-focused.
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: use this for current conditions and forecasts for Australian suburbs. It does not explicitly name alternatives or exclusions, but no sibling tool competes for weather lookups, so the usage context is sufficient.
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.