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

With no annotations, the description carries the behavioral disclosure burden. It usefully reveals the non-obvious filter (only SA1s above 0%) and the data vintage (2021 census). It does not mention coordinate system or share units, but the core behavioral quirk is explicitly disclosed.

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?

One tightly written sentence with the key resource and filter front-loaded. The parenthetical adds essential behavioral detail without padding or repetition.

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?

This is a simple two-parameter GeoJSON fetch with an output schema, so return structure is partly covered. However, the description omits usage guidance and does not clarify what values suburb_name accepts. It is minimally viable for basic invocation 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?

The schema thoroughly documents the geojson parameter, but suburb_name is just a bare string. The description adds only 'in the suburb', which weakly clarifies the parameter's role without format, requiredness, or valid values. At 50% schema coverage, this does not fully compensate for the missing parameter documentation.

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 names the exact resource (SA1 polygons within a suburb) and the specific attribute (2021 census public-housing share), plus the notable >0% filter. This clearly distinguishes it from sibling shape tools such as suburbs_shapes_boundary or suburbs_shapes_mesh_blocks.

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 does not state when to use this tool versus alternatives like properties_surroundings_public_housing or other suburbs_shapes_* endpoints. Usage is only implied by the resource name and the phrase 'in the suburb'; no exclusions or alternative routing are provided.

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