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 states the metric is 'calibrated' but does not explain what calibration means, whether the result is a map, a list, or a time series, or what time period it covers. The description adds a little context but leaves significant ambiguity about the output's nature.

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, well-structured sentence with no wasted words. It front-loads the key information (the metric) and includes the scope (per mesh block) efficiently. This is exemplary conciseness.

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, so the return structure is likely defined there. The description covers the core purpose but omits usage guidance and behavioral nuances like the meaning of 'calibrated.' For a simple parameterized lookup, it is adequate but not fully complete—an agent might need to consult sibling descriptions or examples to know when to choose this over the aggregate vacancy rate.

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 coverage is 0%, so the description should clarify the single parameter. It implicitly does by stating 'in the suburb,' indicating that suburb_name is the target suburb. However, it does not specify the expected format (e.g., full name vs. code) or whether it must match a canonical list. The parameter is self-explanatory by name, but the description does not add meaningful detail beyond that.

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 states the tool returns a calibrated rental-vacancy rate for each mesh block in a suburb. It specifies the resource (suburb) and the metric, and the phrase 'for each mesh block' distinguishes it from the sibling suburbs_market_vacancy_rate, which likely provides an aggregate rate. This is a specific and unambiguous purpose.

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?

No guidance is provided on when to use this tool versus alternatives. The description does not mention that this is the per-mesh-block breakdown of the overall vacancy rate, nor does it point to suburbs_market_vacancy_rate as the aggregate counterpart. An agent would have to infer usage from the name alone.

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