classify_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 discloses that the tool matches against a library of known failure modes and returns structured diagnosis, which implies it is a read-only classification. However, it does not mention any side effects, error handling for unknown errors, or performance characteristics. While adequate for a simple classifier, more detail could improve transparency.

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 two sentences long, front-loaded with the action, and contains no redundant information. Every sentence adds value: the first states the purpose and output, the second provides usage guidance. It is perfectly concise for the tool's simplicity.

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 low complexity (one parameter, no nested objects) and the presence of an output schema (which handles return value documentation), the description is complete. It covers what the tool does, how to use it, and what it returns. No additional details are necessary for an agent to correctly invoke it.

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 input schema has 0% description coverage, meaning the description must add meaning. It does so by specifying that the parameter should be 'a snippet of error text (e.g. from find_errors output).' This clarifies the expected content and source beyond the schema's generic 'string' type. For a single parameter, this provides sufficient semantic context.

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: 'Classify a MOM6/CESM error message and return probable cause and fix suggestions.' It specifies the verb (classify) and the resource (error message), and implies the function of diagnosis. The context of use is clarified by mentioning input from find_errors, which distinguishes it from sibling tools like find_errors that only locate errors.

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 usage guidance by suggesting to 'Pass a snippet of error text (e.g. from find_errors output).' This gives a clear use case and indicates the tool should be used after find_errors. However, it does not explicitly state when not to use it or mention alternatives, but the sibling list and context make the intended workflow clear.

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