databutler-stats
Exact statistics & probability: distributions, hypothesis tests, CIs, Bayesian updates, regression.
我该使用它吗
质量与安全性
发现（1）
- LOWTool 'confidence_interval' description lacks action verb在 confidence_interval 中
基于对工具定义和协议合规性的自动分析。
上下文开销
这是每次将服务器的工具加载到模型上下文窗口时所消耗的大致 token 数。数值越高，可用于其他任务的注意力就越少。
安装
一键安装
将以下内容添加到你的 `claude_desktop_config.json` 文件中：
{
"mcpServers": {
"databutler-stats": {
"url": "https://databutler.dev/api/mcp/stats"
}
}
}远程端点
https://databutler.dev/api/mcp/statsstreamable-http它能做什么
工具清单
工具（6）
⚪descriptive_stats(data)
Summary statistics for a numeric array: mean, median, sd, variance, quartiles, IQR, skewness, min/max.
输入模式
{
"type": "object",
"properties": {
"data": {
"type": "array",
"items": {
"type": "number"
}
}
},
"required": [
"data"
]
}⚪distribution(dist, params, at, p)
Evaluate a probability distribution (normal, t, chi2, binomial, poisson): pdf/pmf and cdf at a value, and/or the quantile at a probability, plus mean & variance. Params per dist: normal {mean,sd}, t {df}, chi2 {df}, binomial {n,p}, poisson {lambda}.
输入模式
{
"type": "object",
"properties": {
"dist": {
"type": "string",
"enum": [
"normal",
"t",
"chi2",
"binomial",
"poisson"
]
},
"params": {
"type": "object"
},
"at": {
"type": "number",
"description": "value to evaluate pdf/pmf and cdf at"
},
"p": {
"type": "number",
"description": "probability to get the quantile for (0-1)"
}
},
"required": [
"dist"
]
}🟢hypothesis_test(test, data, data1, data2, mu0, ...)
Run a significance test and get the statistic, p-value, and a plain-language interpretation with assumptions. test = one-sample-t {data, mu0}, two-sample-t {data1, data2}, one-proportion-z {successes, n, p0}, two-proportion-z {successes1,n1,successes2,n2}, chi2-gof {observed, expected?}, chi2-independence {table}. Optional tail: two-sided (default) | greater | less; alpha default 0.05.
输入模式
{
"type": "object",
"properties": {
"test": {
"type": "string"
},
"data": {
"type": "array",
"items": {
"type": "number"
}
},
"data1": {
"type": "array",
"items": {
"type": "number"
}
},
"data2": {
"type": "array",
"items": {
"type": "number"
}
},
"mu0": {
"type": "number"
},
"successes": {
"type": "number"
},
"n": {
"type": "number"
},
"p0": {
"type": "number"
},
"successes1": {
"type": "number"
},
"n1": {
"type": "number"
},
"successes2": {
"type": "number"
},
"n2": {
"type": "number"
},
"observed": {
"type": "array",
"items": {
"type": "number"
}
},
"expected": {
"type": "array",
"items": {
"type": "number"
}
},
"table": {
"type": "array"
},
"tail": {
"type": "string"
},
"alpha": {
"type": "number"
}
},
"required": [
"test"
]
}⚪confidence_interval(kind, confidence, data, n, mean, ...)
Confidence interval for a mean (t-based; from data, or n/mean/sd) or a proportion (Wilson; successes/n). kind = mean | proportion; confidence default 0.95.
输入模式
{
"type": "object",
"properties": {
"kind": {
"type": "string",
"enum": [
"mean",
"proportion"
]
},
"confidence": {
"type": "number"
},
"data": {
"type": "array",
"items": {
"type": "number"
}
},
"n": {
"type": "number"
},
"mean": {
"type": "number"
},
"sd": {
"type": "number"
},
"successes": {
"type": "number"
}
}
}🟡bayes_update(hypotheses)
Discrete Bayesian update: given competing hypotheses each with a prior and the likelihood of the observed evidence, return normalised posteriors. Priors are renormalised to sum to 1.
输入模式
{
"type": "object",
"properties": {
"hypotheses": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {
"type": "string"
},
"prior": {
"type": "number"
},
"likelihood": {
"type": "number"
}
},
"required": [
"prior",
"likelihood"
]
}
}
},
"required": [
"hypotheses"
]
}⚪linear_regression(x, y)
Simple linear regression of y on x: slope, intercept, r, r², slope std error and p-value, equation.
输入模式
{
"type": "object",
"properties": {
"x": {
"type": "array",
"items": {
"type": "number"
}
},
"y": {
"type": "array",
"items": {
"type": "number"
}
}
},
"required": [
"x",
"y"
]
}社区
证据