ml_train_change_risk

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 the trigger action, not explaining side effects, duration, or what happens to existing models. No contradiction with annotations.

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 extremely concise with one sentence and a '[Write]' tag. No wasted words, but could be more informative 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?

Given no output schema, the description should mention what the tool returns (e.g., training job ID). It lacks information on follow-up actions like checking training history. Sibling tools like ml_model_training_history exist but are not referenced.

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 single parameter solution_name is fully described in the schema (100% coverage) with 'default auto-detect'. The description does not add additional meaning beyond the schema, so baseline 3 applies.

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 uses a specific verb 'Trigger training' and clearly identifies the resource 'change risk prediction ML model'. It distinguishes this training tool from sibling tools like ml_train_anomaly_detector or ml_train_incident_classifier by specifying the model type.

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 on when to use this tool versus alternatives or when not to use it. It does not mention prerequisites, typical scenarios, or exclusions. The description is purely declarative.

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