aiworkhub_quality_calibration_report

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

The 'READ-ONLY' prefix merely restates readOnlyHint=true from the annotations, earning no credit. The only additional trait is 'deterministic', which is genuinely useful, but nothing is said about what the gate matrix evaluates, how results are produced, or any preconditions.

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?

A single front-loaded sentence with no filler and no redundancy. It is as compact as a description can be.

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?

Although an output schema exists and no parameters need explaining, the description is too thin for a tool sitting among many quality/eval siblings. It does not say what the gate matrix checks, when to run it instead of aiworkhub_quality_run_checks, or what a 'calibration report' is.

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?

The tool takes zero parameters, so the baseline is 4. There is nothing for the description to clarify about inputs, and the empty schema is consistent with the definition.

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 names an action (run) and an object (deterministic positive/negative gate matrix), but 'gate matrix' is opaque jargon and the definition offers no differentiation from close siblings such as aiworkhub_quality_run_checks or aiworkhub_eval_artifact_gate. An agent can infer it is a quality check but not what distinguishes it.

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?

There is no when-to-use or when-not-to-use guidance and no named alternative. With roughly a dozen quality-related siblings in the list, the agent is left to guess which one to call.

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