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JeffersonStats

by sharabhshukla
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Python
Remote

JeffersonStats: Advanced Statistical Analysis MCP Server

Version Python License

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