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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), so the description correctly focuses on interpretation instead: scores are precomputed estimates built from O*NET and published research, and are explicitly not predictions of job disappearance. The citation requirement with the returned URL is also a behavioral instruction not derivable from structured fields.

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?

Two sentences, front-loaded with the lookup and its outputs, followed by the caveat and citation rule. Dense but every clause carries information; nothing is redundant.

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?

With no output schema, the description compensates by enumerating the returned components, and the interpretation caveat plus citation instruction covers the main misuse risks. Minor gap: no guidance on what happens when a title is not found or is ambiguous.

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 100% for the single job_title parameter, including an example, so the schema already does the work. The description adds no format or matching semantics (e.g., whether fuzzy matching or alternates are returned), so the baseline 3 applies.

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?

"Look up the AI Exposure Score (0-100) for a job title" is a specific verb plus resource, and the enumeration of returned fields (band, percentile, task categories, adjacent careers) makes the tool's output shape unmistakable. It does not name its siblings (search_occupations, recent_ai_attributed_layoffs), so differentiation is inferred rather than explicit.

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?

Usage is implied by the resource (look up one job title to get its score) but there is no when-to-use vs. when-to-use-something-else guidance, no mention of the sibling tools, and no stated behavior for unknown or ambiguous job titles.

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