one_proportion_z_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 disclosure. It does not mention what the tool returns (e.g., test statistic, p-value, confidence interval), nor does it state any assumptions (e.g., normal approximation). For a statistical test, this is a critical omission that leaves the agent uncertain about the output.

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 a single sentence that is direct and information-dense. Every word contributes to the core purpose. There is no redundancy or filler, making it highly efficient for an AI agent 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?

Given the complexity of a statistical hypothesis test with 4 parameters, no output schema, and many sibling tools, the description is too sparse. It fails to explain the return value, assumptions, or interpretation. An agent would need additional context to use this tool correctly, especially to distinguish it from related tests like chi-square goodness-of-fit.

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 that 'successes out of n' defines the observed proportion and 'p0' is the hypothesized proportion, giving meaning to the three required parameters. However, it does not explain the optional 'alpha' parameter (significance level) or its default value, and it omits constraints (e.g., successes must be between 0 and n). The description adds partial value but is not fully comprehensive.

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 tests whether an observed proportion (successes out of n) differs from a hypothesized proportion p0. The verb 'test' and the resource 'proportion' are specific, and it inherently distinguishes from siblings like two_proportion_z_test (which compares two proportions) and one_sample_t_test (for means).

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?

No explicit guidance on when to use this tool vs. alternatives. The description implies it is for a single proportion, but it does not mention assumptions (e.g., large sample size, independence) or when not to use (e.g., small n, where an exact binomial test might be more appropriate). Given many sibling tools, this lack of differentiation is a significant gap.

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