Crosstabs MCP Server

A Python MCP (Model Context Protocol) server registering 39 statistical tools for contingency-table analysis plus two versioned evidence resources. Individual tools report whether their inference is exact, asymptotic, or simulated.

Features

Core Statistical Tests

Effect Sizes & Measures

Ordinal Measures

Agreement & Reliability

Advanced Analysis

Installation

From PyPI (recommended)

pip install crosstabs

Inspect the public source distribution

python -m pip download --no-deps --no-binary=:all: crosstabs

The development repository is currently private. PyPI publishes the package's source archive; email support@crosstabs.com for issue reports or source-access questions.

Quick Start

Run the MCP Server

crosstabs

Or directly:

python -m crosstabs_mcp.server

Configure Claude Code

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

{
  "mcpServers": {
    "crosstabs": {
      "command": "crosstabs"
    }
  }
}

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: Compare an adverse outcome between exposure groups:
      Exposed: 30 outcome-present, 70 outcome-absent
      Unexposed: 15 outcome-present, 85 outcome-absent

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.

The epidemiology tools (odds_ratio, relative_risk, risk_difference, and attributable_risk) use one explicit orientation: [[exposed outcome+, exposed outcome-], [unexposed outcome+, unexposed outcome-]]. They return machine-readable zero/infinite/undefined states rather than silently continuity-correcting point estimates. Attributable/prevented fractions require causal identification assumptions; an association alone does not establish the counterfactual effect of removing an exposure.

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 = 0.190476 (two-tailed exact)
        Not statistically significant at α=0.05.

Available Tools

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  # Compatibility imports; math lives in server.py
├── tests/
│   ├── test_statistics.py       # Statistical behavior tests
│   └── test_reference_parity.py # Public SciPy reference parity
├── scripts/                    # Distribution verification
├── LICENSE
├── pyproject.toml
├── uv.lock
└── 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

The development repository is currently private. Send corrections and proposed changes to support@crosstabs.com.

Links

• PyPI package and source archive
• Web Application
• Support
• MCP Documentation