Verify savings

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

Annotations (readOnlyHint=false, destructiveHint=false) are consistent with the description's mention of state changes (moving recommendations, recording savings). The description adds useful behavioral context beyond annotations, such as auto-verification in AWS and the status transition.

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 short (two sentences plus examples), front-loaded with the main action, and each sentence adds value. No redundant or irrelevant information.

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?

For a parameterless tool with no output schema, the description adequately explains the input (none needed), behavior (auto-verify, status change, savings recording), and implied output. It is complete for the tool's complexity.

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?

No parameters exist, so schema coverage is 100%. The description adds meaning by explaining what the tool does without needing parameter details, which is appropriate for a parameterless tool.

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 uses a specific verb ('verify'), names the resource ('act-on recommendations'), and explains the mechanism (checking AWS changes, moving statuses). It clearly distinguishes from sibling tools like 'get_rightsizing_recommendations' which focus on listing recommendations, not verifying implementation.

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?

Examples provide clear usage scenarios (e.g., 'Verify our savings', 'Confirm which recommendations actually happened'). The context implies it should be used after acting on recommendations, but it lacks explicit 'when not to use' or comparison to alternatives.

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