salary_ai_risk

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

With no annotations, the description must carry the disclosure burden. It indicates the tool produces risk scores and can accept either occupation or sector, but does not specify output format, behavior when both or neither params are provided, or any computational details. It adds some context but leaves room for ambiguity.

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 a single, information-dense sentence that front-loads the core purpose and enumerates key output aspects. No filler or redundant 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?

The tool is simple (two optional params, no output schema), and the description provides the essential purpose and output dimensions. However, it leaves unstated whether the two parameters can be combined and what is returned if neither is supplied, a gap given the lack of annotations or output schema.

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 input schema already describes both parameters (NACE sector code/keyword, ISCO occupation code/keyword) with 100% coverage. The description only repeats this information ('NACE sector', 'ISCO occupation') without adding new semantic detail, so it meets the baseline but adds no extra value.

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 function ('AI automation risk scores') with clear scope ('by ISCO occupation or NACE sector'). It identifies the output dimensions (exposure, complementarity, risk level), distinguishing it from sibling salary tools like salary_overview which likely provide pay 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?

The description implies use when automation risk for an occupation or sector is needed, but provides no explicit when/when-not guidance or mention of alternative tools. No exclusions or prerequisites are stated, leaving the agent to infer suitability from context.

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