Property crime incidence rates for Australian suburbs: break-and-enter, motor vehicle theft, other theft. Rates are per 100,000 residents, annualised.
| Name | Required | Description | Default |
|---|---|---|---|
| state | No | AU state code: NSW, VIC, QLD, SA, WA, TAS, NT, ACT | |
| suburb | Yes | Suburb name, e.g. "Parramatta" | |
| postcode | No | 4-digit postcode, narrows disambiguation |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the disclosure burden. It usefully adds that rates are per 100,000 residents and annualised, which clarifies the metric type. However, it does not disclose the time period covered, the response shape, or any limitations, leaving some behavioral ambiguity.
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 and front-loaded, naming the dataset, geography, crime types, and units in two short sentences. There is no redundant wording or repetition of 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 three-parameter lookup with fully documented schema, the description captures the essential subject and metric. It does not mention the specific time period or whether results are broken down by crime type, but the tool is simple enough that these gaps are not critical.
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 schema already documents all three parameters. The tool description adds context about crime categories and units, but it does not add meaning specific to the state, suburb, or postcode parameters 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 description clearly identifies the resource: property crime incidence rates for Australian suburbs, with specific crime categories. It lacks an explicit action verb like 'returns' or 'provides', but the subject matter is sufficiently unambiguous and distinct from sibling tools.
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 no guidance on when to use this tool versus alternatives. It does not mention exclusions, disambiguation strategies, or compare itself to related Australian statistics tools, so an agent must infer suitability from the tool name alone.
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.