Monthly building approvals (dwellings, value, by type) at LGA level from ABS BA_LGA2024/2025. Agents frequently ask “how many houses/units approved in this LGA last year?”
| 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, the description carries the burden and does add useful behavior: monthly frequency, LGA-level aggregation, the ABS source, and the measured attributes. Still, it does not disclose the output shape, how suburb maps to an LGA, or the temporal coverage, so the behavioral picture is incomplete.
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 two sentences with no filler. The first sentence front-loads source, frequency, granularity, and content; the second adds a realistic user query that helps agents recognize intent.
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?
The description gives enough domain context for an agent to recognize when this tool applies, but with no output schema it omits response structure and disambiguation behavior. This is adequate but leaves clear gaps for an agent predicting what the tool will return.
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%, with all three parameters (state, suburb, postcode) described inline. The description adds LGA-level context but does not add parameter-specific meaning beyond what the schema already provides, so the baseline score of 3 applies.
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: monthly building approvals with dwellings, value, and type at LGA level from ABS BA_LGA2024/2025. It is differentiated from siblings by content, but it lacks an explicit verb such as 'returns' or 'retrieves' and does not directly distinguish itself from the closely named sibling au-abs-building-activity.
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 example query 'how many houses/units approved in this LGA last year?' implies when this tool is relevant. However, it does not state when not to use it or point to alternatives such as au-abs-building-activity, leaving usage guidance implied rather than explicit.
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.