evaluate_estimator

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

No annotations are provided, so the description must convey behavioral traits. It mentions cross-validation but does not disclose whether the estimator is modified, what the return value is, or any side effects like resource usage. The description is too brief for a tool with no 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 a single, front-loaded sentence with no waste. It is concise but could be more informative while maintaining brevity.

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 performs cross-validation evaluation with three parameters and no output schema or annotations, the description lacks critical context: what the evaluation returns (e.g., scores, plots), how cross-validation is configured (e.g., stratified), and prerequisites (e.g., dataset must be loaded). It is insufficient for an agent to use 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%, so parameters are already described in the schema. The description adds no extra meaning beyond the schema, such as how 'dataset' values relate to available data or how 'cv_folds' affects evaluation. Baseline score of 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 clearly states the verb 'evaluate' with the resource 'estimator' and method 'cross-validation on a dataset', which distinguishes it from sibling tools like 'fit' and 'predict'. It is specific but could be more explicit about what evaluation metrics are used.

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 guidance on when to use this tool versus alternatives (e.g., 'fit', 'predict'), nor does it mention prerequisites like needing an estimator handle from 'instantiate_estimator'. Agents are left to infer context.

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