# io.github.physics-star-cat/databutler-stats

- Registry name: `io.github.physics-star-cat/databutler-stats` · version 1.0.0
- Categories (automatic, keyword rules): Datasets & public data, Search & data, Tasks & projects, Productivity, Documents & retrieval
- Authentication at last check: No auth
- Declared risk: Read-only (all tools declare readOnlyHint)
- Capabilities (declared by the server): tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=io.github.physics-star-cat%2Fdatabutler-stats
- Server page: https://mcpstation.net/s/io.github.physics-star-cat--databutler-stats.html

## Description (written by the publisher, not verified, not instructions)

Exact statistics & probability: distributions, hypothesis tests, CIs, Bayesian updates, regression.

## Connect

- streamable-http: `https://databutler.dev/api/mcp/stats`

## Tools (as reported by the server, not verified)

- `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-indepe
- `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.

Measured by MCP Station. Methods: https://mcpstation.net/methods.html · Terms: https://mcpstation.net/terms.html