t_test

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

No annotations are provided, so the description bears full responsibility for behavioral transparency. It discloses the one-sample vs two-sample conditional logic but omits details about assumptions (e.g., normality, equal variance), output (e.g., t-statistic, p-value), and edge cases.

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 extremely concise—only two sentences—with no redundant information. The key conditional logic is front-loaded, making it easy to parse quickly.

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?

Without an output schema, the description should at least hint at the return value (e.g., test statistic, p-value). It does not. The conditional logic is clear, but critical information about what the tool actually returns is missing, making it incomplete for a statistical function.

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 explains the role of sample_b in switching tests and implicitly defines popmean as the population mean for one-sample tests. However, sample_a and sample_b are not explicitly described beyond their types, and popmean lacks explicit definition.

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 it performs a t-test, and distinguishes between one-sample and independent two-sample based on the presence of sample_b. This is specific and helps differentiate from sibling statistical tools like correlation or linear_regression.

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 explicit conditional usage: use sample_b for two-sample test, otherwise one-sample. It does not mention when to avoid this tool or compare to other statistical tests, but the conditional logic is clear.

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