Jupyter MCP Server Kshitij
This is a Docker - based Jupyter multi - collaboration protocol client project that supports Windows and Mac systems and can be integrated with the Claude desktop for collaborative programming.
rating : 2 points
downloads : 5.4K
What is Jupyter MCP Server Client?
This is a Docker - based Jupyter notebook client that supports multi - user collaborative editing (MCP). It allows teams to collaborate in real - time on the same notebook and can be seamlessly integrated into the Claude desktop environment for use.How to use Jupyter MCP Server Client?
The basic usage process includes: 1) Set up the Python environment. 2) Create a notebook. 3) Start the Jupyter server. 4) Connect via a Docker container. The whole process can be completed through simple command - line operations.Applicable scenarios
It is very suitable for scenarios such as data analysis projects that require multi - person collaboration, team programming teaching, and remote code review, especially when team members use different operating systems (Windows/Mac).Main features
Multi - user real - time collaboration
Supports multiple users editing the same Jupyter notebook simultaneously, with all changes synchronized in real - time
Cross - platform support
Can run on Windows and Mac systems, ensuring environment consistency through Docker
Claude desktop integration
Provides pre - configured JSON configuration for easy integration into the Claude desktop application
Advantages
Simplifies the setup process of collaborative Jupyter notebooks
Solves the environment dependency problem through Docker containerization
Provides an out - of - the - box Claude desktop integration solution
Limitations
Requires pre - installation of Docker and Python environments
The initial setup steps are numerous and may not be friendly to non - technical users
Mac users may encounter WebSocket connection issues
How to use
Set up the Python environment
Create a Python virtual environment and install the necessary dependency packages
Get the project code
Clone the GitHub repository to the local machine
Start the Jupyter server
Run Jupyter Lab and enable the collaboration function
Build the Docker image
Create the client Docker image
Run the client
Start the Docker container to connect to the Jupyter server
Usage cases
Team data analysis
A data science team can analyze the same dataset simultaneously and view each other's analysis results and annotations in real - time
Programming teaching
Teachers can demonstrate code in class, and students can follow in real - time and practice on the same notebook
Frequently Asked Questions
What should Mac users do if they encounter a WebSocket connection error?
How to change the authentication token?
Can the notebook file be placed in other directories?
Related resources
GitHub repository
Project source code and latest updates
Jupyter official documentation
Jupyter usage and configuration guide
Docker installation guide
Docker installation instructions for various platforms

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