Get Occupation Benchmark

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds one genuine behavioral trait beyond them: warnings about unavailable datasets are preserved rather than dropped, telling the agent to expect partial results. It says nothing about size of responses, versioning effects, or failure modes for invalid occupation IDs.

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 compact sentences, front-loaded with the action, then parameter hints, then the warning behavior. Nothing is redundant, though the telegraphic phrasing ("metrics are dataset names") costs a little readability.

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 5-parameter, 0%-coverage tool the description covers only two parameters, and an output schema exists so return values need no explanation. The unexplained dimension and lot_version enums are real gaps an agent cannot resolve from structured fields alone.

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 coverage is 0%, so the description must carry parameter meaning; it explains metrics (dataset names, with salary/demand/specializedSkills examples) and gives region's concrete shape ({"nation":"us"}). It says nothing about the two enum parameters — dimension (lotocc/lotspecocc) and lot_version (6/7) — which are the most ambiguous inputs, nor about occupation_id's expected format.

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?

"Benchmark a LOT occupation" gives a concrete verb plus resource, and the noun pair (occupation + benchmark) is distinct from the metadata/list siblings. The abbreviation "LOT" is never expanded, so an agent must infer it is a taxonomy (likely Lightcast Occupation Taxonomy), which slightly blunts precision.

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 name and description imply usage — retrieve benchmark metrics for one occupation — but there is no explicit when-to-use versus when-not, nor a pointer to alternatives such as query_core_lmi, get_core_lmi_dimension, or the various get_*_metadata tools. Guidance is implied rather than stated.

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