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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered; the description adds genuinely new context: the data source (Anthropic Economic Index), that results are matched from a free-text title, and the important interpretive caveat that this is observed current use rather than a prediction. It omits how matching failure is signalled when a title can't be mapped to an occupation.

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?

It is front-loaded with the invocation trigger and example utterances, then the capability, then the caveat. The enumeration of return fields is a slightly long list, but each item tells the agent what it can offer the user, so the length is largely earned.

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 single-parameter read tool with no output schema, the description covers invocation conditions, data provenance, returned fields, and the main interpretive limitation. The only real gap is what happens when a job title cannot be matched to an occupation.

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?

With one parameter at 100% schema description coverage, the schema already carries the semantics, so the baseline is 3. The description adds only the hint that input is a plain job title that gets matched to an occupation, which implies some input tolerance but gives no detail about ambiguous or unmatched titles.

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 resource and measurement: how much of a job's work AI is already used for, sourced from the Anthropic Economic Index, matched from a plain job title. That is clearly distinct from siblings like search_jobs or score_resume, so an agent can route on it without opening the schema.

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?

It gives explicit trigger phrasing ("is my job safe from AI", "how exposed are accountants") and a clear scope boundary: it measures observed use now, not a forecast of job loss. It stops short of naming an alternative sibling such as find_career_guide for users who want general career information rather than exposure data.

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