statistica_t_test

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

No annotations are provided, so the description carries the full behavioral burden, and it mostly does not. It never says whether the analysis mutates the spreadsheet, what it writes or returns, whether a running STATISTICA instance or a license is needed, or what the `attach` behavior implies for side effects — all notable gaps for a tool that drives an external application.

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?

Two sentences, zero filler, and the purpose is front-loaded before the mode-specific detail. Every clause carries information an agent needs to pick a `kind`.

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 7-parameter analysis tool with no annotations and no output schema, the description covers the core semantics but omits what the tool does to the workbook, what results come back, and any preconditions for running an analysis. It is minimally viable rather than complete.

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 71%, so the schema already documents most parameters, and the description's added semantics for `kind` and `variables` (pairs of 2, 4, ... entries) largely restate the enum and array descriptions. It adds no information about `path`, `sheet`, `attach`, `summary`, or `constant`, so it does not compensate for the coverage gap. Baseline 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 names a specific verb+resource ('Student t-tests') and goes further by defining what each value of the `kind` parameter actually computes (means vs. a constant, paired comparisons over listed variables). This distinguishes it clearly from statistical siblings such as statistica_anova, statistica_correlation, and statistica_normality.

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 gives clear conditional context for the main decision — which test to run — by spelling out the scenario behind each `kind` value. However, it offers no exclusions or named alternatives (e.g., use ANOVA for >2 groups, or a different tool for independent-samples comparisons), so it falls short of the explicit when/when-not bar.

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