crosstabs-mcp 1.0.0

MCP server for statistical crosstabulation analysis with 40+ tools

Crosstabs MCP Server

A Python MCP (Model Context Protocol) server providing 40+ statistical tools for contingency table analysis. Designed for AI assistants like Claude to perform rigorous statistical analysis.

Features

Core Statistical Tests

Test	Description
Chi-square	Pearson's chi-square test of independence
G-test	Likelihood ratio test (more accurate for small samples)
Fisher's exact	Exact test for 2×2 tables
McNemar's	Test for paired categorical data

Effect Sizes & Measures

Measure	Use Case
Cramér's V	Effect size for any table size (with bias correction)
Phi coefficient	Effect size for 2×2 tables
Odds ratio	Association strength with confidence intervals
Relative risk	Risk comparison between groups
Risk difference	Absolute risk reduction
Attributable risk	Population-level impact

Ordinal Measures

Measure	Description
Spearman's rho	Rank correlation
Kendall's tau	Concordance measure
Goodman-Kruskal gamma	Ordinal association
Somers' D	Asymmetric ordinal measure
Stuart's tau-c	Rectangular table measure

Agreement & Reliability

Measure	Description
Cohen's kappa	Inter-rater agreement
Weighted kappa	Agreement with ordinal weights

Advanced Analysis

Tool	Description
CMH test	Stratified analysis controlling confounders
Breslow-Day	Test homogeneity of odds ratios
Correspondence analysis	Dimensionality reduction for tables
Monte Carlo chi-square	Exact p-values via simulation
Power analysis	Sample size and power calculations
Multiple comparisons	Bonferroni and FDR corrections

Installation

From PyPI (recommended)

pip install crosstabs-mcp

From source

git clone https://github.com/barangaroo/crosstabs-lite.git
cd crosstabs-lite/mcp-server-python
pip install -e .

Quick Start

Run the MCP Server

crosstabs-mcp

Or directly:

python -m crosstabs_mcp.server

Configure Claude Code

Add to your ~/.claude/claude_desktop_config.json:

{
  "mcpServers": {
    "crosstabs": {
      "command": "crosstabs-mcp"
    }
  }
}

Or with Python path:

{
  "mcpServers": {
    "crosstabs": {
      "command": "python",
      "args": ["-m", "crosstabs_mcp.server"]
    }
  }
}

Usage Examples

Once configured, Claude can perform statistical analysis:

Chi-square Test

User: Test if there's an association between treatment and outcome:
      Treatment A: 50 success, 30 failure
      Treatment B: 20 success, 40 failure

Claude: [Uses chi_square_test with matrix [[50,30],[20,40]]]
        χ² = 11.67, p = 0.0006
        Cramér's V = 0.29 (small-medium effect)
        There is a significant association between treatment and outcome.

Odds Ratio

User: Calculate the odds ratio for this case-control study:
      Cases: 30 exposed, 70 unexposed
      Controls: 15 exposed, 85 unexposed

Claude: [Uses odds_ratio with matrix [[30,70],[15,85]]]
        OR = 2.43 (95% CI: 1.21-4.87)
        Exposure is associated with 143% higher odds of the outcome.

Fisher's Exact Test

User: I have a small sample: [[3,1],[1,5]]. Is it significant?

Claude: [Uses fishers_exact with the matrix]
        p = 0.103 (two-tailed)
        Not statistically significant at α=0.05.

Available Tools

Tool Name	Description
chi_square_test	Chi-square test of independence
g_test	G-test (likelihood ratio)
fishers_exact	Fisher's exact test (2×2)
mcnemar_test	McNemar's test for paired data
odds_ratio	Odds ratio with CI
relative_risk	Relative risk with CI
risk_difference	Risk difference with CI
cramers_v	Cramér's V effect size
cramers_v_corrected	Bias-corrected Cramér's V
phi_coefficient	Phi for 2×2 tables
cohens_kappa	Cohen's kappa
weighted_kappa	Weighted kappa
spearmans_rho	Spearman's rank correlation
kendalls_tau	Kendall's tau-b
goodman_kruskal_gamma	Gamma coefficient
somers_d	Somers' D
tau_c	Stuart's tau-c
cmh_test	Cochran-Mantel-Haenszel
breslow_day	Breslow-Day test
linear_trend_test	Linear-by-linear association
correspondence_analysis	Correspondence analysis
monte_carlo_chi_square	Monte Carlo exact test
power_analysis	Power/sample size
bonferroni_correction	Bonferroni p-value adjustment
fdr_correction	Benjamini-Hochberg FDR
standardized_residuals	Cell residuals
post_hoc_analysis	Post-hoc chi-square decomposition
proportion_ci	Confidence interval for proportion
check_assumptions	Validate chi-square assumptions
mosaic_plot_data	Data for mosaic visualization
stacked_bar_data	Data for stacked bar chart
attributable_risk	Attributable risk measures
chi_square_yates	Yates' continuity correction
detect_outliers	Outlier detection
crosstab_from_data	Build table from raw data
crosstab_from_csv	Build table from CSV

Development

Run Tests

pip install -e ".[dev]"
pytest tests/ -v

Project Structure

mcp-server-python/
├── crosstabs_mcp/
│   ├── __init__.py
│   ├── server.py          # Main MCP server
│   └── advanced_stats.py  # Additional functions
├── tests/
│   └── test_statistics.py # Test suite (52 tests)
├── pyproject.toml
├── requirements.txt
└── README.md

Requirements

• Python 3.10+
• mcp >= 1.0.0
• fastmcp >= 0.1.0
• numpy >= 1.24.0
• scipy >= 1.10.0
• pandas >= 2.0.0
• statsmodels >= 0.14.0

License

MIT License - see LICENSE for details.

Contributing

Contributions welcome! Please read CONTRIBUTING.md first.

Links

• Main Repository
• Web Application
• MCP Documentation