JJaniel/dataset-analyser-MCP

Dataset Analyzer MCP

This project is a Model Context Protocol (MCP) server designed for intelligent dataset analysis. It provides a suite of tools to help you understand, clean, and process your datasets.

See the file for a log of project updates.

How to Use This Project

This guide will walk you through setting up and running the Dataset Analyzer MCP server on your local machine.

Prerequisites

Before you begin, ensure you have the following installed on your system:

• Git: https://git-scm.com/downloads
• Python: (Version 3.8 or higher) https://www.python.org/downloads/
• uv: (A fast Python package installer) You can install it with pip:

  pip install uv
  

Part 1: For a User (Running the Project)

Follow these steps if you just want to use the server to analyze your own datasets.

Step 1: Get the Code

Clone the repository to your local machine using git. Open your terminal or command prompt and run:

# Use this command for HTTPS (easiest)
git clone https://github.com/JJaniel/dataset-analyser.git

# Or use this command for SSH (if you have SSH keys set up with GitHub)
git clone git@github.com:JJaniel/dataset-analyser.git

This will create a new folder named dataset-analyser. Navigate into it:

cd dataset-analyser

Step 2: Set Up the Python Environment

It's best practice to create a virtual environment to keep project dependencies isolated.

# Create a virtual environment using uv
uv venv

# Activate the environment
# On Windows:
source .venv/Scripts/activate
# On macOS/Linux:
source .venv/bin/activate

# Install the required Python packages
uv pip install -r requirements.txt

Step 3: Configure the Dataset Directory

This server loads datasets from a specific folder. You need to tell it where your datasets are by setting an environment variable named DATASETS_DIR.

• On Windows (in Command Prompt):

  set DATASETS_DIR=C:\path\to\your\datasets
  

• On Windows (in PowerShell):

  $env:DATASETS_DIR="C:\path\to\your\datasets"
  

• On macOS/Linux:

  export DATASETS_DIR=/path/to/your/datasets
  

Replace C:\path\to\your\datasets with the actual, absolute path to the folder containing your .csv files.

Step 4: Run the Server

Now you can start the MCP server:

python main.py

The server is now running and ready to accept connections from an MCP-compatible client.

Part 2: For a Contributor (Making Changes)

Follow these steps if you want to modify the code and contribute your changes back to the main project.

Step 1: Fork the Repository

Go to the project's GitHub page: https://github.com/JJaniel/dataset-analyser Click the "Fork" button in the top-right corner. This creates a personal copy of the repository under your own GitHub account.

Step 2: Clone Your Fork

Clone the repository from your account, not the original. Replace <your-username> with your GitHub username.

git clone https://github.com/<your-username>/dataset-analyser.git
cd dataset-analyser

Step 3: Create a New Branch

It's crucial to make your changes on a new branch to keep them organized.

# Create a branch and switch to it
git checkout -b my-awesome-feature

Replace my-awesome-feature with a short, descriptive name for your change (e.g., add-parquet-support).

Step 4: Make Your Changes

Set up the environment as described in "Part 1" and make your desired code changes using your favorite editor.

Step 5: Commit and Push Your Changes

Once you are happy with your changes, commit them with a clear message and push them to your fork.

# Stage your changes
git add .

# Commit them with a descriptive message
git commit -m "feat: Add support for Parquet files"

# Push the changes to your fork on GitHub
git push origin my-awesome-feature

Step 6: Create a Pull Request

Go to your forked repository on GitHub. You will see a prompt to "Compare & pull request". Click it. This will take you to a page where you can describe your changes and submit them to the original project for review.

License

This project is licensed under the MIT License. See the file for details.