set_ai_error

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 denotes a mutation with '[mutation]' and states it records a mistake, but lacks details on potential side effects, idempotency, auth requirements, or rate limits. Adequate but could be more transparent.

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 a single paragraph that front-loads the purpose and usage. It is clear and structured, though slightly verbose. Every sentence serves a purpose, earning a high score.

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 (3 params, no output schema, no nested objects), the description covers purpose, usage, and parameter context adequately. It provides enough information for an agent to use the tool correctly.

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% with descriptions for all 3 parameters. The description adds minimal new meaning beyond the schema, e.g., 'The correct information or rule to remember' is almost identical. Baseline 3 is appropriate.

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 specifies the verb 'Record', the resource 'AI Correction', and clearly distinguishes from sibling set_customer_ai_error by stating it is for YOUR OWN responses/behavior. The title 'Record AI Correction' reinforces the action.

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 states when to call: 'when the user corrects you' and 'when you proactively detect you did something wrong'. Provides clear exclusion for errors in customer content, directing to set_customer_ai_error. This is excellent differentiation.

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