ml_train_anomaly_detector

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

Annotations indicate a write operation with no idempotency or destructiveness. The description adds only the redundant '[Write]' tag. No disclosure of behavior like whether training is async, how model versioning works, or impact on existing models.

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 sentence is concise and front-loaded. The '[Write]' tag is somewhat redundant but not overly verbose. Slight deduction for wasted space on redundant tag.

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 machine learning training operation, the description is too brief. It omits critical context like whether training is synchronous, expected duration, how to monitor progress, and what output to expect. No output schema exacerbates this gap.

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 description coverage is 100% with clear parameter descriptions. The tool description restates 'table/field' but adds no new meaning. Baseline score of 3 is appropriate as description adds minimal value beyond schema.

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 resource 'anomaly detection model' scoped to a specific table/field. It distinguishes from sibling training tools like ml_train_change_risk and ml_train_incident_classifier which target different models.

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. The description lacks any context about prerequisites, when training is appropriate, or how it differs from other ML training tools.

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