ml_train_incident_classifier

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

The description adds only the '[Write]' tag, which merely duplicates the readOnlyHint=false annotation. It fails to disclose important behavioral traits such as whether training is asynchronous, how long it might take, or what side effects occur (e.g., model replacement). With annotations already covering the write status, the description contributes no extra transparency.

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 a single concise sentence that front-loads the action and resource, with a minimal '[Write]' tag. There is no wasted wording or redundant elaboration, making it highly efficient.

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?

For a simple one-parameter tool with a fully described schema and annotations, the description provides adequate purpose but lacks details about the return value or post-training behavior. Since there is no output schema, the agent does not know what result to expect, leaving a notable gap in completeness.

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 input schema fully describes the single parameter solution_name with its default auto-detect behavior, giving 100% coverage. The description does not add any parameter-specific information, so it earns the baseline score of 3 without further enhancement.

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 the action ('Trigger training') and the specific resource ('incident classification ML solution'), which distinguishes it from sibling ML training tools like ml_train_change_risk or ml_train_anomaly_detector. The verb and resource are precise, leaving no ambiguity about what the tool does.

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 provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, scenarios that warrant retraining, or differentiate from the many other ML training tools in the sibling list, leaving the agent without contextual selection criteria.

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