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. The description adds no additional behavioral context such as output format, reliance on a trained model, or data freshness. 'Using historical ML analysis' is a methodological note, not a behavioral disclosure.

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, front-loaded sentence with no wasted words. Every token contributes to communicating the core purpose, making it highly concise and well-structured.

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 is an ML prediction with no output schema, yet the description fails to explain what the response contains (e.g., risk score, category, confidence). It also does not clarify how the three optional parameters are used together or any prerequisites like a trained model. This under-specifies a complex tool.

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%, so the schema already documents all three parameters. The description does not add any extra meaning beyond the schema, such as which parameters are primary or how they interact. Baseline 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 identifies the tool's action (predict), target (change request), and outcome (risk level), using a specific verb and resource. It distinguishes from ML siblings like ml_train_change_risk and ml_evaluate_model by emphasizing 'predict' rather than 'train' or 'evaluate'.

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 usage guidance is provided. The description does not mention when to use this tool versus alternatives, nor any exclusions, prerequisites, or scenarios. It is not misleading, but offers no directional advice.

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