io.github.physics-star-cat/databutler-stats
io.github.physics-star-cat/databutler-stats · v1.0.0
Exact statistics & probability: distributions, hypothesis tests, CIs, Bayesian updates, regression.
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Last check
Reachable, 6 tools, protocol 2025-06-18 · checked 2026-10-17
Test & rating
A 90/100
What it does: 6 tools · 6 read, 0 write · Documents & PDF, Tasks & projects
Test call confidence_interval: worked (1,043 ms)
How the rating is made
- Answers without login: +30
- Speed of first answer: +10
- Lists tools: +10
- Tool descriptions: +15
- Parameter descriptions: 0
- Read/write annotations: +5
- Safe test call: +20
- More than 80 tools: 0
Tested 2026-10-17. Fixed rules, no AI model. Grade A needs a successful real call.
Install
Remote URL
https://databutler.dev/api/mcp/statsTools
6 tools listed at last check
descriptive_statsSummary statistics for a numeric array: mean, median, sd, variance, quartiles, IQR, skewness, min/max.distributionEvaluate 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_testRun 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-indepeconfidence_intervalConfidence 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_updateDiscrete 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_regressionSimple linear regression of y on x: slope, intercept, r, r², slope std error and p-value, equation.
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