ml_predict_change_risk

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description does not need to repeat that. The description adds the 'predict' nature, but no further behavioral traits (e.g., dependency on a trained model, error conditions). It is consistent 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 a single, concise sentence that immediately conveys the tool's function. No unnecessary words, and it is front-loaded with the key action and resource.

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?

The tool has no output schema, so the description should clarify what the returned risk level looks like (e.g., string, number, structure). It also does not clarify that all parameters are optional but likely need at least one to produce a meaningful prediction. The description is insufficient for a complete understanding.

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 provides full descriptions for all three parameters (type, category, change_sys_id). The description does not add extra meaning or constraints beyond what the schema already states. With 100% schema coverage, the baseline of 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?

The description clearly states the tool's purpose: predicting the risk level of a change request using historical ML analysis. The verb 'predict' and resource 'risk level of a change request' are specific, and it distinguishes from similar ML tools like 'ml_train_change_risk' which focuses on training.

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 information on when to use this tool versus alternatives. Among siblings, there are training and other ML tools, but the description does not provide guidance on selection criteria or context. It implicitly assumes the user knows when to call prediction.

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