detect_anomalies

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

Annotations already indicate read-only, non-destructive, idempotent. The description adds detailed behavioral traits: no side effects, statistical method (z-score analysis), correlation of metrics and logs, and handling of no anomalies (empty list, not an error). No contradictions.

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 cohesive paragraph that effectively front-loads the purpose. It is reasonably concise, though could be slightly more structured (e.g., bullet points).

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?

Despite lacking an output schema, the description fully explains the return value (list of anomalies with fields like service, severity, deviation, explanation) and edge case (empty list). Given the tool's complexity and sufficient annotations, the description is 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 coverage is 100%, so baseline is 3. The description adds context beyond schema by explaining the statistical method (z-score) and the meaning of sensitivity levels, but the schema already describes each parameter well.

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 action ('Scan one or all monitored services for abnormal behavior') and output ('return the findings ranked by severity'), distinguishing it from siblings like get_service_health and query_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?

The description explicitly provides usage context: 'the entry point for 'is anything wrong anywhere?' triage.' It also recommends follow-up actions using get_service_health, query_metrics, and query_logs, and lists related tools.

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