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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint false), the description adds meaningful behavioral context: it never starts a run, incurs no AI cost, and clearly reports an absent matching run instead of silently returning empty results. This helps the agent set user expectations accurately.

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 four sentences with no filler. The core action is front-loaded, followed by concrete use cases and a failure-mode caveat. Every sentence adds decision-relevant value.

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 output schema exists and annotations are rich, the description covers what an agent needs to decide whether to call the tool and what to expect. It provides semantics, exclusions, use-case examples, and the no-run behavior. The only gap is the lack of explicit guidance for state and detail_candidate_ids, but their enum/format definitions mitigate this.

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% (job_id, limit, cursor are described), and the description only partially compensates. It maps output concepts like 'per-criterion verdicts' and 'missing must-haves' to criterion_status and mandatory_verdict, but leaves state and detail_candidate_ids semantically implicit. The parameter names and enums help, but the description does not fully clarify these two 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 opens with a specific verb and resource: 'Reads the stored results of the most recent matching run for a job'. It enumerates the returned content—ranked candidates with scores, per-criterion verdicts, evidence, and missing must-haves—making the tool's purpose unmistakable and distinct 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?

The description explicitly states when this tool is appropriate: reading existing results, exploring 'why is she ranked first', finding candidates who fail a mandatory criterion, or intersecting matches with another attribute. It also gives a clear exclusion—'It never starts or refreshes a run and has no AI cost'—and tells the agent what to do if no run exists: ask the user to run matching in Kepler.

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