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

With no annotations, the description carries the behavioral disclosure burden. It clarifies the time window (90 days), denominator (per 100 dwellings), and rollup levels, which is useful. However, it does not disclose what the returned rows represent, how high/low values should be interpreted, or whether the data is a snapshot.

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, compact sentence that front-loads the core metric and aggregation. Every word adds meaning and there is no redundant filler.

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 one-parameter tool with an output schema, the metric definition is mostly sufficient to understand the tool's purpose. However, it lacks usage context, sibling differentiation, and any caveats about data interpretation, leaving an agent to guess when this tool is the right choice among many market tools.

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 must compensate for the single parameter, suburb_name. It does not mention the parameter at all or clarify expected format (e.g., exact name vs partial). The parameter is inferable from the tool name but not from the description.

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 a specific, well-defined metric ('For-sale listings per 100 dwellings (90 days)') and states the aggregation levels ('per mesh block + suburb rollup'). This distinguishes it from siblings like months_of_supply and stock_on_market, though it lacks an explicit verb.

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 given for when to use this tool versus related market/supply siblings such as suburbs_market_months_of_supply, suburbs_market_stock_on_market, or suburbs_development_supply. The context is implied by the metric name but not stated.

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