Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the transparency burden. It discloses the ABS Census 2021 source, the 2011/2016/2021 census points, and calls the projection tail 'modelled,' which is a useful caveat. It does not explicitly state that the call is read-only or describe error behavior, but the wording clearly implies a data lookup and the data composition is transparent.

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?

Two sentences, front-loaded with the key field and source, followed by a concrete use case. There is no filler, and every sentence earns its place.

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 covers the returned data fields and the projection nature, and an output schema exists to fill in return-shape details. However, the absence of explicit suburb_name semantics is a material gap for a single-parameter tool, and there is no caveat about how the LGA-level series relates to the suburb-level figure.

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 only defines suburb_name as a string with no description, and the tool description provides no parameter contract: no format, no state/territory qualification, and no mention of exact vs fuzzy matching. The Clarkson example hints at a possible value but does not explain how the single argument should be supplied, which is a significant gap at 0% schema description coverage.

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 payload: current ABS Census 2021 population, earlier census points, and an LGA yearly series with a modelled projection tail. It also provides an example question. However, it lacks an explicit retrieval verb like 'returns' or 'lists' and does not directly contrast with the many adjacent demographics sibling tools, so the differentiation is implicit rather than stated.

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 final sentence gives an explicit scenario for when to invoke the route ('what is the total population of Clarkson?'). It does not say when not to use it or name alternative tools, so it falls short of the strongest possible usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.