test_ttest

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

With no annotations, the description carries the full burden and does a solid job: it discloses the full set of reported outputs, the Student vs Welch default behavior, and the nonparametric alternative with effect size. It stops short of mentioning missing-data handling or plot side effects, but covers the core behavioral traits well.

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 well organized: first the output promise, then the three designs in compact bullets, then the nonparametric flag. Every sentence earns its place and there is no filler or repetition of schema-only details.

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 12-parameter statistical tool with no output schema and no annotations, the description is unusually complete. It explains all three test modes, the key parameters that activate each, and the full set of returned statistics, leaving little an agent needs to infer.

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 coverage is 100%, so the baseline is 3; the description adds real value by mapping parameter combinations to the three test designs and explaining the practical meaning of var_equal and nonparametric. This goes beyond the individual parameter descriptions in the schema.

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 opens with a precise verb and resource ('Compare means with a t-test') and immediately lists the exact statistics it will report. The three named designs (one-sample, independent groups, paired) clearly delimit the tool's scope and distinguish it from sibling tests like test_anova or test_proportion.

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 gives explicit design-level usage guidance, showing which parameters correspond to one-sample, independent-group, and paired designs, plus when to set nonparametric=true. It does not explicitly say 'use this for two groups only and use test_anova for more', but the intended scenarios are strongly implied by the three designs.

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