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

No annotations are provided, so the description carries the full behavioral burden. It does disclose that it returns dataflow ids, which is behaviorally important for chaining. However, it doesn't mention result limits, pagination, authentication, or ranking behavior for a search tool with 1,000+ records.

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?

Three short sentences, front-loaded with the core action and scope, followed by when-to-use and return value. Zero waste and highly scannable.

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 single-parameter discovery tool, the description covers purpose, usage context, examples, and return value sufficiently. It lacks output structure details (e.g., what a dataflow id looks like) but is otherwise complete enough for an agent to invoke correctly.

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 parameter is already documented with examples ('wage price index', 'building approvals'). The description adds catalogue context (1,000+ datasets) but doesn't add syntax or format details beyond the schema. Baseline for full coverage plus minor value is a low 4.

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 states a specific verb (Search) and resource (ABS Data API catalogue, 1,000+ datasets) with the keyword mechanism. It clearly distinguishes itself from direct data-fetching siblings like get_cpi, get_labour_force, and abs_get_data by being a discovery tool.

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?

It explicitly names when to use it ('when CPI and Labour Force don't cover the question') and provides concrete example topics (wages, building approvals, population, retail trade, GDP). It also routes the agent forward: 'Returns dataflow ids for abs_describe_dataflow.'

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