sectoral_employment_shift

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

No annotations are provided, so the description carries the full burden. It transparently discloses the output components (per-industry deltas, share, sorted ranking), the calculation method (year-on-year change), and the data source (ABS Labour Force Detailed). It does not mention potential caveats like data revisions, but for a read-only data retrieval tool this is sufficient.

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 concise (three sentences) and front-loaded with the main function. Every sentence adds value: scope, use case, output details, and source. There is no wasted text.

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 zero-parameter tool without an output schema, the description is complete: it explains what the tool returns (deltas, shares, ranking), why it's useful (narrative, career-pivot), and the underlying source. No further context is needed.

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?

The tool takes zero parameters, so the schema is empty. The description adds no parameter-specific instructions, but with no params to explain the baseline of 4 is appropriate; it still indicates the call is self-contained ('Single call...') and describes the result.

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?

Description clearly identifies the tool's purpose with a specific verb+resource: 'Returns the AU sectoral employment shift — year-on-year change in employed persons by ANZSIC industry division.' It also provides a clarifying question it answers ('which industries are hiring and which are shedding?'), distinguishing it from sibling data tools like real_wages or health.

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 gives clear usage context: 'Single call answers...' and notes it's 'Useful for labour-market narrative and career-pivot queries.' However, it does not explicitly mention alternatives or scenarios when not to use the tool, so it doesn't fully meet the 'when-not' criterion for a 5.

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