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stats.ttest_one_sample

Read-onlyIdempotent

Tests whether a sample mean differs from a specified population mean using a one-sample t-test, returning t, degrees of freedom, p-value, and 95% confidence interval.

Instructions

표본 평균이 기준값 popmean과 다른지 일표본 t-검정으로 판정해 t, df(n-1), p_value, ci_95를 돌려준다. values는 Decimal 문자열 2개 이상, popmean은 Decimal 문자열, tail은 two(기본), less, greater이며 p값은 방향에 맞게 scipy가 계산한다. ci_95는 방향과 무관한 표본 평균의 95% t-구간이다. t는 유효숫자 6자리, p값은 10자리 문자열이다. 두 집단 비교에는 ttest_two_sample 을 쓴다.

Input Schema

NameRequiredDescriptionDefault
tailNotwo
valuesYes
popmeanYes

Output Schema

NameRequiredDescriptionDefault
tYes
dfYes
_metaNo서버가 붙이는 영수증(integrity), 엔진, 힌트
ci_95Yes
traceYes계산 근거(tool, formula, inputs, steps, output)
p_valueYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the read-only/idempotent annotations, the description discloses important behavior: minimum sample size, required Decimal-string inputs, scipy-based directional p-value calculation, direction-independent confidence interval behavior, and output precision formats. These details are substantive operational context rather than repetition.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loads the purpose, inputs, outputs, and sibling alternative. Each sentence provides actionable information about constraints, behavior, or output formatting.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description supplies the essential input constraints, directional behavior, return-value semantics, and sibling routing needed to invoke the tool correctly. Since an output schema exists, it need not fully re-document return structure, but it still appropriately summarizes key output behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by documenting all three parameters: values as two-or-more Decimal strings, popmean as a Decimal string, and tail as two/less/greater with a default of two. It also clarifies how tail affects the p-value while leaving ci_95 direction-independent.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the specific tool purpose: a one-sample t-test comparing the sample mean against popmean. It explicitly lists the returned values (t, df, p_value, ci_95) and distinguishes the tool from the two-sample sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly routes two-group comparisons to ttest_two_sample and explains the tail option for one-sided versus two-sided tests. It does not, however, mention paired/repeated-measures alternatives such as ttest_paired, so the exclusion guidance is not exhaustive.

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

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