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

Annotations already establish that this is a safe, non-destructive read operation (readOnlyHint, idempotentHint, destructiveHint=false). The description adds useful context such as quarterly update cadence, 'all ages combined', and source/licensing, but it does not disclose potential surprises like how the dataset behaves for missing quarters or whether larger last_n queries are bounded. This is adequate but not rich behavioral disclosure beyond the annotations.

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 tightly written with no filler. Each sentence contributes something: what is retrieved, what measures are returned, what breakdowns are available, update frequency, and source/licensing. It is front-loaded with the core purpose and does not repeat schema details verbatim.

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?

Given the rich input schema (100% parameter coverage, enums, defaults), useful annotations, and an output schema, this description is complete enough for an agent to select and invoke the tool correctly. It covers the data source, scope, measures, granularity, geography, update frequency, and licensing without requiring the agent to infer anything critical.

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 100%, so the baseline is 3. The description paraphrases the enum values (sex, region, measure) but does not add substantive meaning beyond the schema, which already documents defaults, constraints, and 'from same quarter previous year' semantics. No extra parameter-level guidance is needed, but also none is provided beyond restatement.

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 uses a specific verb ('Retrieve') and names a concrete resource ('quarterly Estimated Resident Population ... from the ABS ERP_Q dataset'), then states exactly what it returns: total counts, annual numeric change, or annual percentage change. It also clarifies the sex and geography breakdowns, making it clearly distinct from the abs.economy.* siblings and other population 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 provides clear context: this tool returns ERP population data for Australia, quarterly, broken down by sex and state/territory. It does not name explicit alternatives or when-not-to-use cases, but the domain-specific detail (personal scope, geography, measure types) is enough for an agent to recognize when this tool is appropriate.

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