gender_pay_context

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 discloses the cross-source nature (WGEA + ABS) and that it's a retrieval operation ('Get'), but it doesn't describe output structure, error behavior, or any limitations. There's no contradiction, but the description is not rich in behavioral detail.

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 (four short sentences) and front-loaded with the main action. Every sentence adds value: the action, the data sources, the use case, and a reminder of the cross-source nature. No wasted words.

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 no output schema and no annotations, the description provides a reasonable high-level picture of what the tool returns (gender pay gap and labour-force composition). It could be more explicit about the response format, but for a simple tool with one optional parameter, it is sufficiently complete for an agent to invoke it 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%, with the industry parameter already documented in detail (accepts name, ANZSIC letter, or code; omit for national). The description adds context by linking the parameter to the reported use case but doesn't significantly extend beyond the schema. Baseline 3 is appropriate.

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 states the tool's function: 'Get the AU gender pay gap with industry context in one call.' It specifies the data sources (WGEA + ABS) and the resource (gender pay gap by industry), distinguishing it from sibling tools like real_wages or get_data by its focused scope and combined data.

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?

Provides explicit when-to-use guidance: 'Use this when an agent is asked what's the pay gap in <industry>?' or comparing AU industries on gender equity.' This is clear context for selection, though it doesn't name alternatives, it gives unambiguous triggers.

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