stats_2var

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

No annotations are provided, so the description carries the full burden. It discloses behavioral constraints such as equal-length x and y, optional parameters, and the list of supported regression models. However, it does not describe output shape, error behavior, or what the computed statistics/regression results actually contain beyond the model list.

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 compact and well-structured with a 'When' header, a 'Params' section, and an example. Every sentence adds value, and the most important usage signal (paired vs single-list) is front-loaded. No filler or repetition.

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 that an output schema exists (so return values need not be described) and the tool is a focused calculator, the description covers the essential aspects: when to use it, the core parameters, model options, and an example. Minor gaps remain around the semantics of freq and the predict arguments, but these are largely self-explanatory or covered by the 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 description coverage is 0%, so the description must compensate. It does so by explaining that x and y must be equal length, listing the supported model values with the default, noting freq/predict_y_at/predict_x_at as optional, and giving a concrete example. This goes well beyond the bare schema, though the meaning of freq and the predict params is left implicit.

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 that this tool handles paired (x,y) stats and regression, and it explicitly distinguishes itself from stats_1var for single-list stats. The main verb is implied rather than stated directly ('When: paired...'), but the resource and operation are unambiguous.

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?

Provides an explicit 'When' clause that tells the agent exactly when to use this tool and names the alternative (stats_1var) for single-list stats. The parameter list and example further clarify usage context, making the decision between siblings clear.

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