stats_1var

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

With no annotations, the description carries the transparency burden. It discloses the optional freq constraint, the effect of norm_x, and the exact formulas for t, P, Q, and R. It does not address edge cases like mismatched lengths or empty data, but the core behavior is clearly specified.

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-organized: a usage statement, parameter list, formula notation, and a concrete example. No sentence is wasted, and the dense notation is appropriate for a calculator tool.

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's simplicity, the presence of an output schema, and no annotations, the description covers purpose, parameters, formulas, and example usage. It is sufficient for an agent to select and invoke the tool correctly without seeking additional context.

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 descriptions are absent (0% coverage), so the description must compensate, and it does: it marks data as required, clarifies freq must be same length, and defines norm_x's role through formulas and an example. Each parameter's meaning goes well beyond the bare type definitions.

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 identifies the resource as 'one-variable summary stats' and the optional normal-distribution t/P/Q/R calculations, which distinguishes it from sibling tools like stats_2var. However, it lacks an explicit verb such as 'compute' or 'calculate,' relying on the noun 'stats' to convey the action.

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?

It opens with 'When: one-variable summary stats...' providing clear applicability. It implies exclusion of two-variable cases by naming 'one-variable,' but does not explicitly name alternatives or state when not to use it.

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