aws_metrics_query

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

The description goes well beyond the annotations (readOnlyHint, idempotentHint) to explain key behaviors: auto-pick period logic, pagination via nextToken/hasMore, response structure, expression vs metric-stat distinction, and how malformed inputs cause downstream errors. This gives the agent a thorough understanding of the tool's behavior.

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 fairly long but well-structured and front-loaded with the main purpose. Each part adds value, though there is slight repetition of period auto-pick details. Overall, it efficiently covers all needed aspects without unnecessary 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 complexity (9 parameters, no output schema), the description is remarkably complete. It explains the response format (series with timestamps/values), pagination, period semantics, and error behavior. Without an output schema, it compensates by describing return fields. Only minor detail like error conditions for missing required fields is omitted, but schema covers that.

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 baseline is 3, but the description adds substantial value beyond schema. It explains how to structure queries (flat array of objects), clarifies startTime/endTime shorthand ('15m', '1h'), details auto-pick period rules, describes maxDataPoints server-side behavior, and explains nextToken usage. This enriches parameter understanding significantly.

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 it queries CloudWatch metrics via GetMetricData, distinguishing it from the legacy get-metric-statistics API. It specifies the resource (CloudWatch metrics) and the action (query), and gives concrete examples like 'show me the CPU on this instance for the last hour'. This differentiates it from sibling tools that handle other AWS actions like aws_resource_list or aws_logs_tail.

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 guidance on when to use this tool, explicitly contrasting with the legacy API and giving example use cases. It does not explicitly state when not to use it, but the sibling tools cover different AWS operations, so context suffices. The examples (CPU, Lambda invocations, expressions) illustrate appropriate scenarios.

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