JeffersonStatsMCP

sharabhshukla/JeffersonStatsMCP

3.2

If you are the rightful owner of JeffersonStatsMCP and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.

JeffersonStats is a high-performance statistical analysis server built on the FastMCP framework, offering a comprehensive suite of statistical tools via a clean API.

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 .

# 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