Databutler Stats

Last 30 days

Uptime

100%

Response time

141 ms typical, 141 ms slowest 5%

Last check

5 h ago

Next check

in 1 h

How to call it

Add it to any MCP client that supports remote servers.

{
  "mcpServers": {
    "databutler-stats": {
      "type": "http",
      "url": "https://databutler.dev/api/mcp/stats"
    }
  }
}

6 tools

• 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, exp

• 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.

Security scan

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