set_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 behavioral burden. It includes '[mutation]' to indicate a write operation and defines the scope clearly. However, it does not disclose side effects (e.g., whether the correction is applied immediately or queued) or any error states, leaving some gaps.

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 with a clear header, followed by usage rules and an important note. Every sentence adds value without redundancy. It is concise yet comprehensive.

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 lack of output schema and annotations, the description covers purpose, usage boundaries, and sibling differentiation. However, it omits details about the result of recording (e.g., does it return a confirmation?) and any prerequisites, leaving minor completeness gaps.

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 100%, and the description adds no additional meaning beyond what the schema already provides. Baseline 3 is appropriate since the schema handles parameter documentation adequately.

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 that the tool records a correction for customer-facing WhatsApp AI content. It specifies the exact scope (wrong info, tone, unnecessary questions) and distinguishes it from the sibling tool set_ai_error, which handles behavioral rules.

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?

Explicitly tells when to use (only for content corrections) and when not to use (behavioral rules, referencing set_ai_error). The italicized note further clarifies a common misinterpretation, leaving no ambiguity.

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