ml_model_evaluation

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

With no annotations, the description carries full burden. It details return format (markdown report with sample count), metric types per model, and the binary requirement for classification. However, it does not address error handling like mismatched array lengths or non-binary classification inputs.

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?

Three efficient sentences, each serving a distinct purpose: main action, metric details per type, and alternative guidance. No waste, well-organized.

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 4 parameters fully described in schema and no output schema, the description covers return details, alternative tools, and metric computation logic. Minor gap: does not explicitly state array length requirements, though implied by 'same length/order' in schema.

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%, so baseline is 3. The description adds value by explaining metric families per modelType, default metrics, and that only matching metrics are computed, which goes beyond the schema's simple enum listing.

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 evaluates existing model predictions against actual values, differentiates between classification and regression, and specifies the returned metrics. It also distinguishes from sibling tool advanced_regression_analysis.

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 explicitly advises using this tool for scoring supplied predictions and directs to advanced_regression_analysis for fitting models from raw data, providing clear guidance on when to use this vs alternatives.

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