JeffersonStats: Advanced Statistical Analysis MCP Server

Overview

JeffersonStats is a powerful, high-performance statistical analysis server built on the FastMCP framework. It provides a comprehensive suite of statistical tools accessible via a clean, intuitive API. Whether you're performing basic descriptive statistics or advanced statistical tests, JeffersonStats delivers accurate results with minimal configuration.

Features

JeffersonStats offers a rich set of statistical capabilities:

Basic Statistics

• Mean, median, mode, and range calculations
• Standard deviation and variance
• Quartiles and interquartile range (IQR)
• Percentile and quantile calculations

Advanced Statistics

• Skewness and kurtosis analysis
• Correlation coefficients (Pearson, Spearman, Kendall's tau)
• Covariance calculations
• Z-score transformations

Hypothesis Testing

• T-tests (one-sample, independent, paired)
• ANOVA (Analysis of Variance)
• Chi-square tests
• Mann-Whitney U test
• Wilcoxon signed-rank test
• Normality tests (Shapiro-Wilk)
• Binomial tests

Data Analysis

• Linear regression
• Confidence intervals (standard and bootstrap)
• Outlier detection
• Moving averages
• Frequency tables
• Comprehensive descriptive statistics summaries

Why Choose JeffersonStats?

• High Performance: Built on optimized NumPy and SciPy libraries for fast computation
• Easy Integration: Simple HTTP API that works with any programming language or platform
• Comprehensive: Over 30 statistical tools in a single package
• Reliable: Based on industry-standard statistical implementations
• Containerized: Easy deployment with Docker
• Scalable: Designed to handle large datasets efficiently

Installation

Using Python

# Clone the repository
git clone https://github.com/yourusername/JeffersonStats.git
cd JeffersonStats

# Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run the server
python mcpserver.py

Using Docker

# Clone the repository
git clone https://github.com/yourusername/JeffersonStats.git
cd JeffersonStats

# Build the Docker image
docker build -t jeffersonstats:latest .

# Run the container
docker run -p 8080:8080 jeffersonstats

The server will be available at http://localhost:8080.

Usage

JeffersonStats exposes its statistical tools through a MCP server using streamble-http transport. Here are some examples:

MCP Clients supported

• CherryStudio
• VSCode
• Cursor
• WindSurf
• BlackGoose

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with FastMCP
• Statistical calculations powered by NumPy and SciPy