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

With no annotations provided, the description carries the disclosure burden. It explains what the response contains, but it does not mention data sources, caveats, or any operational behavior beyond the returned content.

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 one compact sentence with no filler or repetition. The primary output, ABN industry-mix pattern, is front-loaded and the additional POI content is appended efficiently.

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 single required parameter and with an output schema present, the description conveys the main response themes and includes concrete examples of expected content. It leaves some domain terminology like ABN to the schema or user knowledge, which is reasonable.

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 only parameter is suburb_name, which is self-evident and reinforced by the phrase 'in the suburb' in the description. No format or validation details are added, but for a single obvious string parameter this is acceptable.

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 identifies a specific data product: an ABN industry-mix classification plus notable POI categories with named examples. This clearly distinguishes it from demographics siblings focused on income, age, unemployment, and similar topics.

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?

There is no guidance about when to use this tool instead of related tools such as suburbs_demographics_all or suburbs_lifestyle_pois. The agent must infer usage from the tool name and description alone.

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