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

There are no annotations, so the description carries the burden of explaining behavior. It does describe the output shape and units: each row contains the suburb's value and the capital-growth differential in percentage points. It does not state whether the tool is read-only, how rows are ordered, or what happens when no matched studies exist, but these are relatively minor for this kind of query tool.

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 compact and front-loaded with the core purpose. The second paragraph adds only essential detail about row structure, units, and coverage categories. No filler or redundant wording.

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 description covers the tool's main function and data scope, and an output schema exists to define return fields. However, it lacks parameter-format guidance and sibling differentiation, and it does not explain what 'matched studies' means or how the attribution should be interpreted. These gaps limit completeness for an agent navigating a large sibling set.

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 for the parameter's meaning. It provides only indirect context ('for the suburb', 'suburb's own value') and does not explain what format suburb_name should take, whether it must match a canonical suburb list, or any validation requirements. This is a clear gap given the 0% schema coverage.

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 identifies a specific analytical product: per-indicator capital-growth attribution. It states that rows pair a suburb's own indicator value with the attributed growth differential, and lists the categories covered. It does not use an explicit verb like 'returns' or 'computes', and it does not explicitly distinguish itself from similar sibling tools such as suburbs_market_price_growth or suburbs_risks_capital_growth_impact, so it misses a top score.

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 a use case for per-indicator capital-growth attribution, but it provides no explicit guidance on when to prefer this tool over alternatives, nor does it mention exclusions or prerequisites. With a large sibling list that includes several market-growth and risk-impact tools, some routing guidance would be valuable.

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