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

Annotations already declare read-only, non-destructive, closed-world behavior, and the description adds important context beyond that: it assembles context only, does not validate against live market demand or hiring volume, and leaves tiered recommendations to the client model. This prevents the agent from expecting tool-generated recommendations or market validation.

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 definition is front-loaded with purpose and then adds the key scope boundaries and division of labor. The sentences are dense but each contributes routing or expectation-setting information, with little waste.

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 the annotations and output schema, the description supplies everything an agent needs: what context is assembled, how it differs from get_job_fit_evidence, that it is read-only, that recommendations are client-produced, and that live market validation is out of scope.

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%, so all four optional parameters are documented in the schema. The description does not map or elaborate on those parameters beyond the general profile context, so it appropriately stays at the baseline for high schema coverage.

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 action — assembling profile, experience, career journal evidence, and CareerCompass data — to recommend role titles, and explicitly contrasts this with get_job_fit_evidence. An agent can distinguish it from the sibling evaluator tool without opening either 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 names the key alternative, get_job_fit_evidence, and explains the inverse selection condition: start from the candidate profile rather than evaluating one specific job. It also clearly limits scope to role taxonomy and excludes live job-market validation.

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