get_rds_rightsizing_recommendations

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

No annotations are provided, so the description carries the full burden. It explains the data source (CloudWatch CPUUtilization over 14 days), the exclusion criteria, and the output type (downsizing recommendations with estimated savings). However, it lacks details about authentication requirements, rate limits, or the exact output format. The description is adequate but not exhaustive.

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 well-structured: a concise purpose statement, followed by implementation details, parameter list, and examples. It is efficient, with no redundant information. Every sentence adds value.

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 has 2 parameters, no output schema, and no annotations, the description covers the core aspects: purpose, data source, exclusions, parameters, and example queries. It does not describe the return format, but the mention of 'downsizing recommendations with estimated savings' gives a general idea. For a specific recommendation tool, it is fairly 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 0%, so the description compensates well. It explains both parameters under 'Args:': cpu_threshold (default 20%) and regions (defaults to all opted-in regions). The examples further clarify usage. This adds meaningful context beyond the schema's type and default values.

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 purpose: 'Detect over-provisioned RDS instances with low CPU utilization.' It specifies the resource (RDS instances) and the action (detect over-provisioned), and distinguishes itself from siblings by focusing on RDS CPU utilization, with exclusions like Aurora Serverless and read replicas. This is a specific verb+resource combination that sets it apart.

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 example queries like 'Which RDS instances are over-provisioned?' indicating usage scenarios. It also states what is excluded (Aurora Serverless, read replicas). However, it does not explicitly mention when not to use this tool versus siblings like get_idle_rds_instances or get_rightsizing_recommendations, but the context is clear enough for an AI agent.

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