Databutler Stats
Exact statistics & probability: distributions, hypothesis tests, CIs, Bayesian updates, regression.
Connect it
Settings → Connectors → Add custom connector
URL: https://databutler.dev/api/mcp/statsSettings → Apps & Connectors → Advanced → Developer mode
Create connector with URL: https://databutler.dev/api/mcp/stats{
"mcpServers": {
"databutler-stats": {
"url": "https://databutler.dev/api/mcp/stats"
}
}
}claude mcp add --transport http databutler-stats https://databutler.dev/api/mcp/stats{
"servers": {
"databutler-stats": {
"type": "http",
"url": "https://databutler.dev/api/mcp/stats"
}
}
}6 tools, verified live
descriptive_statsread-onlySummary statistics for a numeric array: mean, median, sd, variance, quartiles, IQR, skewness, min/max.
distributionread-onlyEvaluate 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_testread-onlyRun 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_intervalread-onlyConfidence 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_updateread-onlyDiscrete 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_regressionread-onlySimple linear regression of y on x: slope, intercept, r, r², slope std error and p-value, equation.
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