compare_models

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

With no annotations provided, the description carries full burden for behavioral disclosure. It only states the core function; it does not describe side effects, output format, or whether results are saved (though parameters hint at saving). The description adds little beyond the basic purpose.

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 that efficiently conveys the main function. It is concise without unnecessary words, earning a high score, though a slightly expanded explanation could improve usefulness.

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 complexity of 12 parameters and no output schema, the description is insufficient. It does not explain the return value, side effects, or how parameters like save_results and generate_learning_curves affect behavior. The tool's overall functionality is not fully described.

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?

Since schema coverage is 100%, the baseline is 3. The description itself adds minimal insight beyond the schema; for example, 'statistical significance testing' hints at some parameters but does not provide new meaning. The description does not compensate for low coverage because coverage is already high.

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 uses the specific verb 'compare' and identifies the resource as 'multiple trained models on the same dataset.' It adds the distinctive element of 'statistical significance testing,' which helps differentiate it from sibling tools like compare_datasets or compare_runs.

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 implies usage for comparing multiple models, but it does not explicitly state when to use this tool versus alternatives (e.g., evaluate_model for single models) or mention prerequisites like models must already be trained. No explicit when-not-to-use guidance is provided.

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