get_anomalies

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

No annotations provided, so description carries full burden. Discloses that anomalies are unacknowledged, includes deviations field, and explains parameter behavior (sigma default, since clamping). However, does not mention pagination, result limits, or authentication requirements, leaving some gaps.

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 concise sentences with front-loaded purpose and immediate usage examples. No wasted words; every sentence contributes to understanding.

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?

Given no output schema and only two simple parameters, the description covers the core behavior and parameter semantics well. Lacks info on pagination or result limits, but for a straightforward list tool it is largely 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 covers 100% of parameters. Description adds valuable context beyond schema: explains sigma default and sensitivity, since parameter range clamping and default. Agent gains practical insight for parameter tuning.

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?

Clearly states the action (List) and resource (unacknowledged cost/latency/error-rate anomalies). The description distinguishes this tool from siblings like get_savings, get_stats, etc., which focus on different metrics.

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?

Provides explicit usage guidance with example user queries ('anything weird going on?', 'any spikes?', quick health check). Lacks explicit when-not-to-use or alternative tool references, but context is clear given sibling list.

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