ml_detect_anomalies

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

Annotations already declare readOnlyHint and openWorldHint, covering safety aspects. The description adds context about scope (operational metrics) but does not disclose return format, limitations, or any behavioral details beyond what annotations imply. No contradiction exists.

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, well-constructed sentence that front-loads the core action and provides illustrative examples. Every word earns its place with no redundant information.

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 description, combined with the detailed schema and annotations, is mostly sufficient for understanding and invoking the tool. However, it does not mention that a model must be trained first (given the sibling ml_train_anomaly_detector), which could be a relevant prerequisite. Return format is also unspecified, but the lack of an output schema and read-only nature mitigate this.

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 schema provides 100% coverage with descriptions for all four parameters, including defaults and examples. The description adds no additional parameter semantics beyond naming metric types, so it does not exceed the baseline for high schema coverage.

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 ('Run anomaly detection') and the resource ('operational metrics'), with concrete examples like alert volume and incident trends. It distinguishes well from siblings like ml_train_anomaly_detector, which is about training rather than running detection.

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 explicit guidance is given on when to use this tool versus alternatives such as ml_train_anomaly_detector. The detection vs training context is implied but not stated, and there is no mention of prerequisites or when not to use the tool.

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