# Physics Star Cat Databutler Stats MCP server

Source: https://glimind.com/server/mcp-registry%2Fio.github.physics-star-cat%2Fdatabutler-stats
Registry id: `mcp-registry/io.github.physics-star-cat/databutler-stats`
Status: auth-required (checked 2026-10-27T10:38:41.235Z)

## Measurements (30 days)

- Uptime: 100%
- Checks: 4866
- Outages (90 days): 0

## Install

```json
{
  "mcpServers": {
    "physics-star-cat-databutler-stats-mcp-se": {
      "type": "http",
      "url": "https://databutler.dev/api/mcp/stats"
    }
  }
}
```

Claude Code:

```sh
claude mcp add --transport http physics-star-cat-databutler-stats-mcp-se https://databutler.dev/api/mcp/stats
```

## Tools (6)

- `bayes_update`: Discrete Bayesian update: given competing hypotheses each with a prior and the likelihood of the observed evidence, return normalised posteriors. Priors are ren
- `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.
- `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
- `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 {dat
- `linear_regression`: Simple linear regression of y on x: slope, intercept, r, r², slope std error and p-value, equation.

Measured by Glimind (https://glimind.com): MCP handshake + tool list only, no tool is ever called. CC BY 4.0.