compare_predictions

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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: the tool returns agreement status, confidence levels, and feature analysis in plain English. However, it does not mention potential limitations (e.g., model availability, sample range), error handling, or performance characteristics like rate limits, 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by return details and usage rationale, with a clear 'Args' section. Every sentence adds value without redundancy, making it efficient and well-structured.

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 moderate complexity (3 parameters, no output schema, no annotations), the description is largely complete: it covers purpose, parameters, and return values. However, without an output schema, it could benefit from more detail on the return format (e.g., structure of the plain English explanation), slightly reducing completeness.

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 description coverage is 0%, so the description must compensate. It adds significant meaning beyond the schema by explaining each parameter's purpose with examples (e.g., model IDs like 'gbc_lubricant_quality', sample index as 'row index in the test dataset'), clarifying their roles in the comparison process effectively.

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 with specific verbs ('compare', 'explain') and resources ('what two models predict for the same sample'). It distinguishes from siblings like 'explain_prediction' (single model) and 'compare_features' (features rather than predictions), making the scope explicit.

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 clear context for usage ('to build trust in predictions by checking cross-model consistency'), but does not explicitly state when not to use it or name specific alternatives among siblings. It implies usage for model comparison rather than other tasks, but lacks explicit exclusions.

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