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

Annotations declare readOnlyHint=true and idempotentHint=true, and the description reinforces this with 'It never starts or refreshes a run and has no AI cost.' It also discloses the behavior when no run exists ('says so'), adding context beyond annotations. No contradiction found.

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 description is well-structured: it front-loads the primary purpose, then gives concrete usage examples, and ends with edge-case handling. Every sentence carries value, with no filler or redundancy.

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 tool has an output schema and annotations, the description covers the main behavior, usage scenarios, and the no-run case. However, it does not explain the filtering parameters (state, criterion_status, mandatory_verdict, detail_candidate_ids) at all, which is a notable gap for a tool with 7 parameters. Despite that, the core functionality is well covered, so it is slightly above average but not fully complete.

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 description coverage is only 43%, so the description should compensate by explaining the parameters' roles. It does not; it only mentions 'ranked candidates with scores, per-criterion verdicts, evidence, and missing must-haves' without mapping these to specific parameters like state, criterion_status, mandatory_verdict, or detail_candidate_ids. The description adds no meaning beyond what the schema already provides for the covered parameters.

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 clearly states it reads stored matching results for a job, enumerates the returned data (ranked candidates, scores, verdicts, evidence, missing must-haves), and explicitly contrasts with starting a run. It also gives concrete example queries ('why is she ranked first', 'who fails a mandatory criterion'), making the purpose unambiguous and distinguishable from sibling search tools.

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 explicitly states when to use it (e.g., to explain rankings, find missing criteria, intersect matches) and when not to (it never starts or refreshes a run). It also provides guidance for the no-run case, directing the user to run matching in Kepler. This is explicit, actionable usage guidance.

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