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

No annotations are provided, so the description carries the full burden. It discloses the metric is labour-force-weighted and compares to a pinned national median, which is useful. However, it doesn't disclose what 'pinned national median' means, whether the data is a snapshot or time series, how 'microburb' is defined, or any caveats about data availability. For a data tool with no annotations, this is a moderate gap.

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 a single sentence that packs the key metric, comparison baseline, and granularity. It's concise and front-loaded with the main output. However, it could be slightly clearer with a verb like 'Returns' or 'Provides'.

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 tool has an output schema (not shown) and a single parameter, so the description doesn't need to explain return values. But given no annotations and 0% schema coverage, the description should clarify the meaning of 'pinned national median' and 'microburb' to make the tool fully usable. It's adequate but not complete.

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 0%, so the description must compensate. The description mentions 'suburb' and 'microburb' but doesn't explain the format or constraints of suburb_name (e.g., exact name, case sensitivity, whether it accepts IDs). The single parameter is required, but the description adds little beyond the schema's property name.

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 states a specific metric (labour-force-weighted unemployment rate) and the comparison context (against pinned national median), plus the per-microburb shares. It clearly identifies the resource (suburb demographics unemployment) and the verb is implied by the tool name. It doesn't explicitly name a sibling alternative, but the metric is specific enough to distinguish from other demographics 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 implies the tool is for unemployment data for a suburb, but it doesn't explicitly state when to use it vs alternatives like suburbs_demographics_all or suburbs_demographics_income. The context signal of sibling tools provides some differentiation, but the description itself offers no explicit when/when-not guidance.

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