ml_train_incident_classifier

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

Annotations indicate a write operation (readOnlyHint false) and non-destructive (destructiveHint false). The description adds only '[Write]', which is redundant with annotations. No disclosure of side effects, duration, or model state changes beyond what annotations provide.

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 sentence plus a tag, which is concise but borderline under-specified. It earns its place without waste, but could be expanded with additional context without losing conciseness.

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?

As a training trigger with no output schema, the description lacks information on return values, training duration, how to check status, or prerequisites. Sibling 'ml_model_training_history' exists but is not referenced, leaving the agent under-informed.

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 parameter 'solution_name' has a description ('ML solution name (default auto-detect)') providing full coverage. The tool description adds no extra meaning beyond the schema, so baseline 3 is appropriate.

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?

Description clearly states 'Trigger training of the incident classification ML solution', specifying the verb 'Trigger training' and the resource 'incident classification ML solution'. It distinguishes the tool from non-ML tools but not from sibling ML training tools like 'ml_train_anomaly_detector' or 'ml_train_change_risk'.

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?

No guidance is given on when to use this tool, prerequisites, or alternatives. With siblings like 'ml_evaluate_model' and 'ml_model_training_history', the agent receives no context on choosing this tool over others.

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