rigor

Verified statistical inference for AI agents.

LLMs are decent at reciting statistics but bad at doing it reliably — a t-statistic or a required sample size is a number recalled from training data, not computed and checked. rigor is the alternative: classical hypothesis testing, effect sizes, power/sample-size calculation, and multiple-comparisons correction, computed from scratch and returned as a cited, assumption-checked answer.

Built as an MCP server: a scan of the current MCP ecosystem (Context7 for coding docs, several physics/engineering/chemistry/geo servers, even Bentley's STAAD integration) found statistics/experimental design as one of the few common agent needs nobody had covered yet.

No dependencies beyond the Python 3 standard library, except for the MCP server itself (rigor/mcp_server.py), which needs the official mcp SDK.

Install

pip install rigor-mcp          # CLI only
pip install "rigor-mcp[mcp]"   # CLI + the MCP server

(the PyPI distribution is rigor-mcp since plain rigor was already taken by an unrelated package; the importable package and the CLI command are both still just rigor.)

What's in it

• rigor/distributions.py — t, chi-squared, and F distributions built from scratch on stdlib (regularized incomplete gamma/beta), verified against exact closed-form identities (t(1) = Cauchy, chi2(2) = scaled exponential, t² = F(1, df)) rather than trusted transcription.
• rigor/inference.py — one-/two-sample and paired t-tests, one-/two-proportion z-tests, chi-squared goodness-of-fit and independence, one-way ANOVA. Each returns a TestResult: statistic, degrees of freedom, two-tailed p-value, a confidence interval, a citation, and assumption warnings (e.g. small-n normality reliance, low expected cell counts).
• rigor/effect_size.py — Cohen's d, Hedges' g, Cohen's h, Cramér's V.
• rigor/power.py — power and required sample size for the two-sample t-test and two-proportion z-test. The two directions (given n, find power; given power, find n) are exact numerical inverses of each other by construction (bisection on the same underlying power function), and sanity-checked against the Cohen (1988) d=0.5/α=.05/power=.80 textbook reference case (n≈64).
• rigor/corrections.py — Bonferroni and Benjamini-Hochberg (FDR) multiple-comparisons correction.
• rigor/cli.py — a CLI over all of the above (rigor.py at the repo root is a thin shim so python3 rigor.py ... also works from a plain checkout, without installing anything).
• rigor/mcp_server.py — an MCP tool wrapper exposing all 17 operations to any MCP client (Claude Code, Claude Desktop, etc.). Smoke-tested end-to-end over stdio against a real client — tool discovery plus representative calls checked against known reference values, including the full round-trip still landing the Cohen (1988) case at n=63.

Usage

CLI, once installed:

rigor ttest one-sample --data 5.1,4.9,5.3,5.0,4.8,5.2 --mu0 5.0
rigor power ttest-2samp --effect-size 0.5 --power 0.8
rigor --help   # full list of subcommands (ttest, ztest, chi2, anova, effect-size, power, correct)

or straight from a checkout without installing anything:

python3 rigor.py ttest one-sample --data 5.1,4.9,5.3,5.0,4.8,5.2 --mu0 5.0

MCP server, over stdio (the transport local clients like Claude Code expect):

pip install "rigor-mcp[mcp]"
rigor-mcp

or from a checkout: pip install mcp && python3 -m rigor.mcp_server.

Register it with Claude Code:

claude mcp add rigor -- rigor-mcp

(or, from a checkout: claude mcp add rigor -- python3 -m rigor.mcp_server, run from this repo's root or with an absolute module path). For interactive poking with the MCP Inspector, run it as a script rather than the installed command — which means the package root has to be put on the path by hand, since the Inspector imports the file directly:

pip install "mcp[cli]"
PYTHONPATH=. mcp dev rigor/mcp_server.py

A transport-level edge case, handled

cohens_d correctly returns +inf/-inf for zero-variance samples (per its own documented contract), but non-finite floats serialize to JSON null over MCP's structured content — which used to fail the tool's own number-typed output schema and crash the call. The MCP cohens_d tool now returns {"value": float | null, "warnings": [...]} instead of a bare float, so that case is reported explicitly (null value, a warning naming the direction) rather than blowing up. Every other numeric tool here is bounded and always finite for valid input, so this treatment is specific to cohens_d.

Tests

python3 -m unittest discover -s tests -v

70 tests: 65 exercise the statistics directly; 5 spawn mcp_server.py as a real MCP client would and check results over the wire (skipped automatically if mcp isn't installed).

License

MIT — see LICENSE.