one_sample_t_test

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

The description discloses that the tool returns the t-statistic, degrees of freedom, two-tailed p-value, a confidence interval for the mean, and any assumption warnings. This provides useful behavioral context beyond the parameter schema, especially since no annotations are provided. It covers key output aspects without needing to mention destructive or read-only hints.

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 two sentences long, front-loading the core purpose in the first sentence and listing return values in the second. Every part is essential and clearly phrased.

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 low parameter count (3), no annotations, and no output schema, the description covers the tool's purpose and outputs well. It lacks explicit assumption warnings in the description (though it mentions 'assumption warnings' as part of output), but the statistical context is clear enough for an agent familiar with t-tests.

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?

With 0% schema description coverage, the description must compensate. It does so by explaining 'mu0' as a hypothesized value and implicitly linking 'data' to the sample. It does not detail 'alpha', but its default value (0.05) is common knowledge for significance level. Overall, the description adds meaningful meaning beyond the bare parameter list.

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 the tool tests whether a sample's mean differs from a hypothesized value mu0, which is a specific verb+resource pair. It distinguishes this from sibling tools like two_sample_t_test and paired_t_test by explicitly mentioning a single sample and a hypothesized value.

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 implicitly indicates when to use this tool (for testing a single sample mean against a hypothesized value) but provides no explicit guidance on when not to use it or alternatives. It does not mention prerequisites such as normality or independence assumptions, which are common in t-tests.

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