Train the change risk prediction ML model to update its risk assessments. Use when you need to retrain the model with current change data.
Trigger training of the change risk prediction ML model. [Write]
| Name | Required | Description | Default |
|---|---|---|---|
| solution_name | No | ML solution name (default auto-detect) |
Changes observed during successful MCP inspections.
v1.0.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate this is a write operation (readOnlyHint=false) and not idempotent. Description adds that it 'triggers training', which implies a state change, but does not disclose potential async behavior, resource impact, or effects on existing model versions. This is acceptable given annotation coverage but not rich.
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?
Single clear sentence, directly front-loaded with verb and object. No filler or redundancy; the trailing '[Write]' is a separate classification, not part of the description.
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?
Tool has no output schema and one optional param. Description covers the core action but does not mention what the response looks like, whether training is synchronous or asynchronous, or if any confirmation/job ID is returned. Given the simplicity, this is adequate but not thorough.
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 coverage is 100% with a self-descriptive parameter 'solution_name' (default auto-detect). Description adds no parameter info, but the schema already sufficiently documents the single optional parameter, 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?
Description uses specific verb 'Trigger training' and identifies the exact resource 'change risk prediction ML model'. It clearly distinguishes from sibling tools like ml_predict_change_risk or ml_train_anomaly_detector by naming 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?
Implied usage: call when you need to train/retrain the change risk model. However, no explicit when-to-use or alternatives are mentioned, such as using ml_predict_change_risk for prediction or ml_evaluate_model for evaluation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.