sklearn_add_evaluation

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

There are no annotations, so the description carries the full burden of disclosing side effects, permissions, or output behavior. It only says 'Generate model evaluation framework' and 'Pro feature', with no mention of whether files are written, whether an existing sklearn project is required, or what an evaluation framework actually consists of.

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 very short and front-loaded with the core action and resource. The 'Pro feature' note is useful context and the sentence contains no wasted words, though it is so brief that it sacrifices useful detail.

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?

For a simple tool with no output schema and no annotations, the description is still under-specified: no return value, no effect on the project, no indication of what api_key is for, and no relation to sibling sklearn tools. An agent can guess the basic purpose but would not be confident about how to invoke it 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?

The description adds no meaning to the parameters: api_key is completely undocumented and the schema only says directory is 'Project directory'. With 50% schema coverage, the description needed to compensate, especially for api_key, but it does not.

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 states a specific action ('Generate') and a resource ('model evaluation framework'), which is sufficiently clear for a sklearn evaluation tool. It is not a tautology and is distinguishable from sibling tools like sklearn_generate_pipeline and sklearn_add_preprocessing by the 'evaluation' focus, though it does not explicitly name any alternative.

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?

No guidance is given about when to use this tool versus sklearn_generate_pipeline, sklearn_add_preprocessing, or other model-related tools. The only contextual signal is 'Pro feature', which hints at licensing constraints but not at usage conditions or prerequisites.

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