databutler-stats

Exact statistics and probability for AI agents — use instead of computing these in your head, where language models are unreliable. Distributions (normal/t/chi2/binomial/poisson pdf-cdf-quantile), hypothesis tests (t, z, proportion, chi-square, with p-value + assumptions), confidence intervals, discrete Bayesian updates, and linear regression. Numerical core validated against reference tables. Free, no auth. From databutler.dev.

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接入信息

传输形态: http
鉴权方式: 鉴权未知
端点: https://databutler-stats--drchriseames.run.tools

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{
  "mcpServers": {
    "databutler-stats": {
      "url": "https://databutler-stats--drchriseames.run.tools"
    }
  }
}

能力清单

工具	说明
descriptive_stats	Summary statistics for a numeric array: mean, median, sd, variance, quartiles, IQR, skewness, min/max.
distribution	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}.
hypothesis_test	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.
confidence_interval	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.
bayes_update	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.
linear_regression	Simple linear regression of y on x: slope, intercept, r, r², slope std error and p-value, equation.

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