Extract job criteria

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

Annotations cover the safety profile (readOnlyHint=true), so the description's job is to add operational context — and it does: the 1-credit cost, language-agnostic input, and the shape of the emitted criteria (weight 1-3, mandatory flag, rationale). That is meaningful disclosure beyond the annotations, though nothing is said about limits on size or failure modes.

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 tight sentences with zero filler; the transformation and output shape are front-loaded before the downstream hint and cost note.

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?

No output schema exists, so the description compensates by naming the returned fields and their value ranges, plus the credit cost and the follow-up tool. An agent has everything needed to call it and interpret the result.

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?

Only one parameter and schema coverage is 100%, so the schema already documents job_text fully. The description adds 'any language' as a semantic constraint on the input, which is a small but real addition — baseline 3 is appropriate here.

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?

Specific verb (extract) and resource (job criteria) with the transformation spelled out: job description text in, weighted matching_criteria[] out. It names the downstream consumer (match_candidate), so an agent can place it in the pipeline 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?

States the downstream step explicitly ('Feed the result to match_candidate') and discloses the credit cost, which is real usage guidance. It stops short of saying when NOT to use it or how it differs from siblings like parse_resume, but the intended pipeline position is clear.

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