reasonforge-statistics 0.1.1

Probability and data science MCP server - 16 tools for statistics

ReasonForge Statistics

Probability, statistics, and data science - 16 tools

An MCP (Model Context Protocol) server that provides Claude with statistical and probabilistic reasoning capabilities using SymPy's symbolic engine.

Beta Status: This package is functional but still undergoing testing. Some edge cases may not be fully covered. Please report issues on GitHub.

Capabilities

• Probability Theory - Symbolic probability calculations and distributions
• Statistical Inference - Bayesian inference, hypothesis testing, confidence intervals
• Regression Analysis - Symbolic regression and correlation
• Data Science - Statistical moments, time series, ANOVA, multivariate statistics

Installation

pip install reasonforge-statistics

Or install from source:

git clone https://github.com/foxintheloop/ReasonForge.git
cd ReasonForge
pip install -e packages/reasonforge -e packages/reasonforge-statistics

Claude Desktop Configuration

Add to your Claude Desktop config file:

Windows: %APPDATA%\Claude\claude_desktop_config.json macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "reasonforge-statistics": {
      "command": "python",
      "args": ["-m", "reasonforge_statistics"]
    }
  }
}

Tools

Probability (3 tools)

Tool	Description	Example Use
calculate_probability	Compute symbolic probabilities	P(A and B) given P(A), P(B)
probability_distributions	Work with probability distributions	Normal, Poisson, Binomial
distribution_properties	Get distribution properties	Mean, variance, MGF

Statistical Inference (4 tools)

Tool	Description	Example Use
bayesian_inference	Perform Bayesian updates	Posterior from prior and likelihood
statistical_test	Conduct statistical tests	t-test, chi-square
hypothesis_test_symbolic	Symbolic hypothesis testing	Power analysis
confidence_intervals	Compute confidence intervals	95% CI for mean

Regression (2 tools)

Tool	Description	Example Use
regression_symbolic	Symbolic regression analysis	Fit polynomial models
correlation_analysis	Compute correlation coefficients	Pearson, Spearman

Data Science (7 tools)

Tool	Description	Example Use
symbolic_dataframe	Symbolic data manipulation	Transform data symbolically
statistical_moments_symbolic	Compute moments of distributions	Skewness, kurtosis
time_series_symbolic	Time series analysis	Trend, seasonality
anova_symbolic	Analysis of variance	One-way, two-way ANOVA
multivariate_statistics	Multivariate analysis	PCA, factor analysis
sampling_distributions	Sampling distribution theory	CLT applications
experimental_design	Design of experiments	Factorial designs

Example Usage

Once configured, you can ask Claude:

Probability:

• "What is the probability of getting at least 3 heads in 5 coin flips?"
• "Calculate the expected value of a Poisson distribution with lambda = 5"

Inference:

• "Perform a Bayesian update: prior is Beta(2,2), observed 7 successes in 10 trials"
• "Calculate the 95% confidence interval for a sample mean"

Regression:

• "Fit a quadratic model to these data points: (1,2), (2,5), (3,10), (4,17)"
• "What is the correlation coefficient for these paired observations?"

Data Analysis:

• "Compute the skewness and kurtosis of a normal distribution"
• "Set up a one-way ANOVA for comparing three treatment groups"

Dependencies

• Python >= 3.10
• mcp >= 1.0.0
• sympy >= 1.12
• reasonforge (core library)

Running Tests

pytest packages/reasonforge-statistics/tests/ -v

License

MIT License - See LICENSE for details.

Related Packages

• reasonforge - Core symbolic computation library
• reasonforge-algebra - Matrix operations for statistics
• reasonforge-analysis - Calculus for probability theory
• reasonforge-logic - Logical reasoning