t_test_one_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 mentions the statistical test but does not describe key behaviors such as output format (e.g., t-statistic, p-value), assumptions (e.g., normality), or error handling. For a tool with no annotations and three parameters, this leaves significant gaps in understanding how it operates.

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 and front-loaded, consisting of a single sentence that directly states the tool's purpose. There is no wasted text, and it efficiently communicates the core functionality without unnecessary elaboration.

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 complexity of a statistical test, three undocumented parameters, no annotations, and no output schema, the description is incomplete. It lacks details on inputs, outputs, assumptions, and usage context, making it inadequate for an agent to reliably invoke the tool without additional information.

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?

The schema description coverage is 0%, meaning none of the parameters ('data', 'population_mean', 'alpha') are documented in the schema. The description does not add any meaning beyond the schema—it does not explain what these parameters represent, their units, or valid ranges. This fails to compensate for the lack of schema documentation.

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 one-sample t-test to test whether sample mean differs significantly from population mean.' It includes a specific verb ('Perform'), resource ('one-sample t-test'), and statistical context. However, it does not explicitly differentiate from sibling tools like 't_test_two_sample' or 'paired_t_test', which prevents a perfect score.

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, only stating the domain ('statistics') and category ('inference'). It does not specify when to use this tool versus alternatives (e.g., 't_test_two_sample' for comparing two samples) or any prerequisites. Without explicit when/when-not instructions, it offers limited practical guidance.

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