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math_statistics

Perform statistical calculations including descriptive statistics, correlation, linear regression, and probability distributions from data or distribution parameters.

Instructions

统计与概率计算。

data: 数据集或分布描述。 数据集格式: '[1,2,3,4,5]' 或 '1,2,3,4,5'。 配对数据(回归/相关): '[[1,2],[3,4],[5,6]]' 或 '[1,2;3,4;5,6]'。 operation: describe — 描述性统计(均值、中位数、标准差、极值等) mean / median — 均值 / 中位数 std / variance — 标准差 / 方差 min / max / range — 最小值 / 最大值 / 极差 skewness / kurtosis — 偏度 / 峰度 quantile — 分位数(需 quantile 参数,如 0.25) correlation — Pearson 相关系数(需配对数据) linear_regression — 简单线性回归 y=ax+b(需配对数据) distribution: 概率分布查询,格式 '分布名,参数1=值,参数2=值,x=点'。 支持的分布: normal, exponential, binomial, poisson, uniform, gamma, beta, chi2, t, F。 示例: 'normal,mu=0,sigma=1,x=1.96' 操作: pdf, cdf, quantile, mean, variance, sample(需 n=数量)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
operationNodescribe

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description bears the full burden of transparency. It describes operations and distribution queries but does not mention potential errors, side effects, or statelessness. For a computation tool, the coverage is adequate but not exhaustive.

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

Conciseness4/5

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

The description is moderately lengthy but well-structured with bullet-like formatting for operations and distributions. It front-loads the purpose and efficiently conveys necessary details without redundancy.

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

Completeness4/5

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

Given the complexity (multiple operations, distribution queries), the description is comprehensive. It covers data formats, operation types, and distribution parameters. The presence of an output schema reduces the need to document return values, but missing error or constraint information slightly lowers completeness.

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?

The input schema has 0% description coverage, so the description fully compensates by explaining the 'data' format (including examples for both single and paired data) and the 'operation' parameter with all available options and default. This adds substantial meaning beyond the schema.

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 clearly states '统计与概率计算' (statistics and probability calculation), distinguishing it from sibling tools like math_calculus and math_eval. It lists operations and distributions, making the tool's scope explicit.

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

Usage Guidelines3/5

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

The description explains data formats and operation options but does not provide explicit guidance on when to use this tool versus alternatives. There are no when-to-use or when-not-to-use statements, though the detail implies usage for statistical and probability tasks.

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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