audit_cloudwatch_metric_cardinality

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

No annotations provided, so description must disclose behavioral traits. It mentions that it scans regions and defaults to opted-in regions, but does not explicitly state that the operation is read-only or discuss permissions. The cost context adds some transparency but side effects are unclear.

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 concise with no superfluous information. It includes a clear main statement, relevant cost context, parameter description, and example queries—all in a well-structured format.

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 (one optional parameter) and presence of an output schema, the description provides sufficient context for an agent to understand the tool's purpose and when to invoke it.

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 schema has 0% description coverage, but the description's Args section explains that 'regions' specifies AWS regions to scan and defaults to all opted-in regions. This adds meaning beyond the schema's type definition.

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 audits CloudWatch custom metric cardinality, explains cost implications, and provides example queries. It distinguishes itself from sibling CloudWatch audit tools (e.g., audit_cloudwatch_logs_ia_opportunities) by focusing specifically on metric cardinality.

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 includes example queries that suggest appropriate use cases (e.g., identifying namespaces with too many custom metrics or costly CloudWatch metrics). However, it does not explicitly state when not to use or compare to alternatives like scan_cloudwatch_waste.

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