ATO Taxation Statistics by postcode: median and mean taxable income, individuals lodging returns, and median net tax. Annual release (~1 year lag), currently 2022-23. High demand for property and market analysis agents.
| Name | Required | Description | Default |
|---|---|---|---|
| postcode | Yes | 4-digit Australian postcode |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It discloses data vintage and update frequency, but does not explicitly state read-only behavior, output format, or behavior for missing/invalid postcodes.
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 well-structured: core resource and fields first, then data vintage, then use cases. Every sentence adds relevant context and there is no redundant wording.
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 lookup, the description covers the resource, the returned statistics, temporal coverage, and likely use cases. It lacks only explicit notes on invalid postcodes or missing data, which are minor for this simple tool.
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 single parameter 'postcode' is already fully described in the schema as a 4-digit Australian postcode. The description merely reuses 'by postcode' and adds no format, example, or edge-case guidance beyond the schema.
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 an ATO taxation statistics resource for postcodes and enumerates its specific data fields (median/mean taxable income, individuals lodging returns, median net tax). It differentiates from siblings by geographical/statistical scope, but lacks an explicit action verb such as 'returns' or 'retrieves'.
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?
It provides a clear intended-use context ('High demand for property and market analysis agents') and useful timing context (annual release, ~1 year lag, 2022-23), but does not explicitly state when to prefer this over alternatives like nz-income or au-abs-demographics.
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.