ml_evaluate_model

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

Annotations already mark it as read-only (readOnlyHint=true) and open-world (openWorldHint=true). The description adds that it returns accuracy, training status, and metrics, but does not reveal additional traits like data freshness or permission requirements. It does not contradict 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 a single concise sentence that effectively communicates the tool's purpose without unnecessary detail.

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?

While the description mentions key outputs (accuracy, training status, metrics), it lacks specifics on return format or additional details. Given no output schema, it could be more explicit but is minimally adequate.

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 only parameter, model_sys_id, is fully described in the schema ('ML solution sys_id'). The description does not add extra meaning beyond the schema, so baseline score 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 clearly states the tool retrieves accuracy, training status, and metrics for a trained ML solution. It uses a specific verb ('Get') and identifies the resource, though it could be more precise about what 'metrics' includes.

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 provided on when to use this tool versus alternatives like ml_model_training_history or ml_predict_change_risk. The description does not differentiate it from sibling ML tools.

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