two_sample_t_test

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

With no annotations provided, the description carries full burden. It discloses key default behavior (Welch's test, no equal variance assumption) and the alternative variant (equal_var=true). It could further describe return type or hypothesis test details, but given the common statistical test, this is sufficient.

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, no extraneous words. The first sentence states the core purpose, and the second provides critical default behavior and a conditional use case. Every sentence earns its place.

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 simple function (t-test), no output schema is expected. The description covers purpose, default behavior, and a key option. It lacks explicit mention of return value structure (e.g., statistic, p-value) but for a well-known test, this is acceptable. Completeness is high for the tool's complexity.

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 for all four parameters. The description explains 'equal_var' (default false means Welch's test) and 'alpha' is implied by the test context, though not explicitly described. The required arrays 'a' and 'b' are clear from the test definition. Some minor improvement could explicitly list all parameters with their roles.

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 uses a specific verb ('Test') and resource ('two independent samples') with the clear goal of determining if means differ. It also distinguishes itself from siblings like 'paired_t_test' and 'one_sample_t_test' by specifying 'independent' samples.

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 clear context for when to use this tool (comparing means of two independent samples) and when to choose the classic pooled-variance test (set equal_var=true). It does not explicitly exclude alternatives like 'one_way_anova' for more than two groups, but the sibling context makes this implied.

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