hypothesis_test_ttest

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

No annotations are provided, so the description carries full responsibility for disclosing behavior. It only states that the tool performs a t-test and returns a dictionary, but it does not specify the test type (e.g., two-tailed vs. one-tailed), the exact keys in the result dictionary, or any assumptions like normality. This lacks essential behavioral context beyond the obvious.

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, starting with the core action, followed by a well-organized Args list, and closing with Returns. Every line serves a purpose, and there is no redundant wording. The structure is clear and scannable, ideal for AI parsing.

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 lack of an output schema and annotations, the description should thoroughly explain what the tool does and returns. It only says 'Dictionary with test results' without detailing the dictionary keys or the statistical hypothesis tested. The tool is nontrivial (statistical inference), and this minimal description leaves many questions unanswered, such as whether it supports one-sided tests or what the default alpha is (though the schema sets a default). The description is inadequate for full autonomous use.

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 schema description coverage at 0%, the description must compensate for the sparse schema. It adds brief explanations for each parameter: 'Sample data for testing,' 'Hypothesized population mean,' and 'Significance level.' This goes beyond the schema's bare names and types, but the explanations are shallow and do not specify data formats, typical alpha values, or how sample_data should be structured (though the schema indicates an array). The description adds some meaning but not enough to fully disambiguate.

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 'Perform one-sample t-test,' which identifies a specific statistical operation with a precise resource. It is distinct from sibling tools such as binomial_probability and correlation_analysis, making the tool's purpose unmistakable.

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 no guidance on when to use this tool versus alternatives. It does not mention scenarios where a one-sample t-test is appropriate, nor does it mention any exclusions or preferred conditions. Users must rely on the tool name alone, which is insufficient for decision-making among the listed statistical siblings.

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