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

Annotations already mark it as read-only and non-destructive. The description adds anomaly type examples but does not disclose default behavior (e.g., whether dismissed alerts are excluded by default) or return structure. No contradiction 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?

Two sentences with no filler: the first states the function, the second supplies illustrative examples. Every word earns its place.

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 simple with optional params and no output schema. The description explains the domain of alerts but is vague about 'recent', default filtering, and the exact fields returned. More details on the response format would make it more complete.

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 parameters (limit, severity, dismissed) are fully documented in the schema. The description adds no extra parameter meaning, so the baseline score of 3 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 'recent anomaly alerts detected across metrics' and gives concrete examples of alert types (engagement drops/spikes, unusual spend patterns, content performance outliers). This specific verb+resource framing distinguishes it from other get_ tools like get_analytics.

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?

The description implies use when checking for anomalies, but it does not explicitly mention when to prefer this tool over alternatives (e.g., get_analytics) or provide when-not-to-use conditions. The context is clear but lacks explicit exclusionary guidance.

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