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rigor-mcp

by mrnh
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    Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

    TDQS

    A4.1/5.0

    Scored across 37 tools

    Disambiguation5/5

    Every tool targets a distinct statistical procedure or design (e.g., paired vs. independent proportions, fixed-sample vs. sequential means, chi-square vs. exact test), and the descriptions explicitly cross-reference sibling tools to prevent misselection. Even the paired power/sample-size tools are clearly differentiated as inverse operations.

    Naming Consistency5/5

    All tool names use consistent snake_case and follow a predictable pattern: tests are named after the procedure (e.g., two_sample_t_test, mann_whitney_u), effect sizes after the statistic, and power/sample-size tools share the power_for_/sample_size_for_ prefix. The few non-test utilities (recommend_test, naive_peeking_inflation) are still stylistically consistent.

    Tool Count2/5

    37 tools is well beyond the 25+ threshold for a heavy surface, even though each tool is individually useful. The inclusion of recommend_test and pairwise_group_comparisons mitigates the burden, but the set would benefit from consolidation or a more focused scope.

    Completeness4/5

    The suite covers the common lifecycle of a statistical analysis: assumption checks, parametric/nonparametric tests, effect sizes, power/sample-size planning, multiple-comparison corrections, and pairwise follow-ups. Minor gaps remain, such as no power/sample-size calculations for ANOVA or chi-square and no explicit normality test, but agents can work around these.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

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