rca_model_validate

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

The description adds value beyond annotations by disclosing that the tool sets the model status to 'validated' and computes specific metrics (coverage, mean_correlation, confidence). Annotations (readOnlyHint=false, destructiveHint=false) are consistent and provide an additional baseline. The description does not contradict annotations.

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 plus a clean args/returns block. It is front-loaded with the primary action and efficiently conveys purpose, computation, and side effect without redundancy.

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?

The description covers purpose, side effects, and return format, but lacks context on prerequisites, error behavior, and integration with other model tools. For a validation tool with moderate complexity, more completeness would be beneficial.

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 0% (though schema itself has some descriptions for two fields), and the tool description only lists parameter names in the args block without adding constraints, types, or explanations. The description does not compensate for the lack of schema descriptions.

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 action (validate), resource (model), method (run quick validation on hold-out data), and outcomes (computes coverage/confidence metrics, sets status to validated). It clearly distinguishes from siblings like rca_model_update_status.

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 no explicit guidance on when to use this tool versus alternatives. It mentions 'quick validation' but does not state prerequisites (e.g., model must exist) or when not to use it. Context signals and annotations do not compensate.

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