delete_customer_ai_error

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only indicates mutation via '[mutation]' but lacks details on side effects, reversibility, or error handling (e.g., what happens if the ID is invalid or if dependencies exist).

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 one sentence plus a '[mutation]' tag, making it concise and front-loaded. Every word serves a purpose, though it could be slightly more detailed without becoming verbose.

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 simple delete operation with one parameter and no output schema, the description adequately explains what resource is affected and when it is appropriate. It is complete for the tool's complexity, though it could mention if the action is irreversible.

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 covers 100% of parameters with a clear description for the single 'id' parameter. The tool description adds no additional meaning beyond the schema, resulting in a baseline score of 3.

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clearly states the action (delete), the resource (correction from customer-facing WhatsApp AI), and the context (no longer valid). It effectively distinguishes from sibling tools like 'delete_ai_error' which targets a different type of error.

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 implies when to use: when a correction is no longer valid. However, it does not explicitly state when not to use or mention alternatives like 'update_customer_ai_error' or 'set_customer_ai_error', which are present among siblings.

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