summarize_metric_anomalies

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

With no annotations, the description carries the transparency burden. It discloses the algorithm (rolling z-score), the output style (metric statistics with anomaly windows flagged), and a key limitation (no raw time series). This goes beyond a simple statement and gives behavioral context.

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 exactly two sentences, front-loaded with the main purpose followed by a clarifying detail about output and algorithm. Every word earns its place, with no redundant fluff.

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 the tool's simplicity (2 params, no annotations, presence of an output schema), the description is sufficiently complete. It explains what the tool does, how it flags anomalies, and what it returns, leaving only minor gaps around error cases or edge behavior that are likely covered by the output schema.

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 description does not explicitly explain the parameters model_name or lookback_hours, and schema coverage is 0%. However, the parameter names are self-explanatory and the schema provides the default for lookback_hours. The description adds little parametric meaning beyond what the schema already communicates.

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 verb 'summarize' and the resource 'serving-metric anomalies' with specific metric types (latency, error rate, prediction stats). It distinguishes itself from sibling tools by focusing on anomaly summarization rather than rollback, incidents, drift, or deployment history.

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 provides clear context by noting it is 'suitable for an LLM to reason over without raw time series,' which implies when to use it. However, it does not explicitly name alternatives or state when not to use the tool, stopping short of a 5.

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