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

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

- name: io.github.physics-star-cat/databutler-stats
- version: 1.0.0
- connection class: R0 (autonomous)
- trust: 90/100 flags: duplicate-repo
- quality: 86/100 (good)
- owner: not claimed
- descriptor: https://api.protogrid.dev/v1/servers/io.github.physics-star-cat%2Fdatabutler-stats
- tools: https://api.protogrid.dev/v1/servers/io.github.physics-star-cat%2Fdatabutler-stats/tools

**An agent can connect right now, no human step.** Remote endpoint, no authentication, reachable on the last probe.

## Connection (mcpServers)

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


## Trust

- hygiene: 80
- liveness: 100
- freshness: 85
- provenance: 90
- drivers: +repository +namespace-matches-repo +website +reachable +uptime-100% ~updated-50d-ago -duplicate-repo(16 names)
- Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed.

## Quality 86/100 (good)

### protocol: 75 (weight 25)

- warn `protocol.modern`: newest supported version is 2025-11-25; 2026-07-28 not supported, older versions are deprecated until 2027-07-28
- n/a `protocol.stateless`: only applies to 2026-07-28 servers
- pass `protocol.transport`: streamable HTTP
- n/a `protocol.list_ttl`: only applies to 2026-07-28 servers

### authorization: n/a (weight 15)

- n/a `auth.prm`, `auth.as_metadata`, `auth.cimd`: no remote uses OAuth
- n/a `auth.secret_in_url`: no templated URL

### tool hygiene: 100 (weight 30)

- pass `tools.descriptions`: every tool has a description
- pass `tools.description_length`: descriptions are concise
- pass `tools.schemas`: every tool has an object input schema
- pass `tools.annotations`: 6 of 6 tools declare readOnlyHint or destructiveHint
- pass `tools.directory_hints`: every tool declares readOnlyHint, destructiveHint, idempotentHint and openWorldHint
- pass `tools.token_cost`: about 918 tokens to load every tool
- pass `tools.api_dump`: tools are not a one-to-one API dump

### stability: 75 (weight 15)

- warn `stability.changes`: 6 tool changes in 30 days
- pass `stability.rug_pull`: no tool changed its meaning under the same name

### dependencies: n/a (weight 15)

- n/a `deps.known_vulns`, `deps.mcp_sdk_version`, `deps.resolvable`: no npm or PyPI package

- tools: 6, about 918 tokens to load them all
- tool set hash: c15d2c7ad6cc716a314ae84d23fff29c
- tools last changed: 2026-10-03T13:35:37.152Z
- badge: [![protogrid quality](https://protogrid.dev/badge/io.github.physics-star-cat/databutler-stats.svg)](https://protogrid.dev/servers/io.github.physics-star-cat/databutler-stats)
- Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed.

### Tool changes

- 2026-10-03 `linear_regression` changed (title, annotations)
- 2026-10-03 `bayes_update` changed (title, annotations)
- 2026-10-03 `confidence_interval` changed (title, annotations)
- 2026-10-03 `hypothesis_test` changed (title, annotations)
- 2026-10-03 `distribution` changed (title, annotations)
- 2026-10-03 `descriptive_stats` changed (title, annotations)

Full history: https://api.protogrid.dev/v1/servers/io.github.physics-star-cat%2Fdatabutler-stats/changes

## Remotes

- https://databutler.dev/api/mcp/stats (streamable-http, auth none, reachable true, uptime30d 1)

## Packages

_none_

## 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 renormalised to sum to 1.
- `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. 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-independence {table}. Optional tail: two-sided (default) | greater | less; alpha default 0.05.
- `linear_regression`: Simple linear regression of y on x: slope, intercept, r, r², slope std error and p-value, equation.