t_test_two_sample

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool performs a statistical test but lacks details on output format (e.g., returns p-value, test statistic), error handling (e.g., invalid input), or computational behavior (e.g., handling of large datasets). For a tool with no annotations, this is a significant gap in transparency.

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 concise and front-loaded, with a single sentence that directly states the tool's function. It avoids unnecessary words, though it could be more structured (e.g., separating purpose from context). The parenthetical additions (Domain, Category) are brief and relevant, contributing to efficiency without clutter.

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 complexity (statistical inference with 4 parameters), no annotations, and no output schema, the description is incomplete. It lacks details on output (e.g., what statistical values are returned), assumptions (e.g., normality, independence), and error conditions. For a tool with moderate complexity and no structured support, the description should provide more comprehensive guidance to be fully helpful.

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 mentions 'two-sample t-test (independent samples)', which implicitly relates to 'data1' and 'data2' parameters, and 'test whether two sample means differ significantly', hinting at the 'alpha' parameter for significance level. However, it doesn't explain parameter roles (e.g., 'equal_variance' for Welch's test) or data requirements (e.g., numeric arrays). The description adds some meaning but doesn't fully cover the 4 parameters.

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's purpose: 'Perform two-sample t-test (independent samples) to test whether two sample means differ significantly.' It specifies the statistical test type, sample independence, and the goal of comparing means. However, it doesn't explicitly differentiate from sibling tools like 'paired_t_test' or 't_test_one_sample', which are related statistical tests.

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 provides minimal usage guidance. It mentions the domain (statistics) and category (inference), which implies context, but offers no explicit advice on when to use this tool versus alternatives like 'paired_t_test' or 'mann_whitney_u' (a non-parametric alternative). There's no mention of prerequisites, assumptions (e.g., normality), or scenarios where this test is appropriate.

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