🚀 Jupyter MCP Server

What is 🚀 Jupyter MCP Server?

This server bridges AI agents (e.g., Claude) with Jupyter notebooks, enabling real‑time code execution, visualization generation, advanced image extraction (PNG/JPEG), and full notebook management through the Model Context Protocol (MCP). It is a fork of Datalayer’s original project, enhanced with improved documentation, error handling, and additional tools.

How to use 🚀 Jupyter MCP Server?

Prerequisites: Python 3.11+, Jupyter Notebook, and a compatible MCP client (e.g., Claude Desktop).
Install: Clone the repository, run pip install -r requirements.txt, then start Jupyter with the provided script (./scripts/start_jupyter.sh).
Configure Claude Desktop by adding the following to claude_desktop_config.json:

{
  "mcpServers": {
    "jupyter": {
      "command": "python",
      "args": ["/path/to/your/jupyter-mcp-server/src/jupyter_mcp_server.py"]
    }
  }
}

Run python3 scripts/get_claude_config.py to generate the correct configuration with current paths.

Key features of 🚀 Jupyter MCP Server

• 18 comprehensive MCP tools for code execution, cell manipulation, and notebook operations.
• Real‑time Jupyter integration via WebSocket for live code execution and kernel management.
• Advanced image extraction – supports PNG and JPEG from matplotlib, seaborn, plotly.
• Full notebook file operations – create, delete, switch, and list notebooks; CRUD on cells.
• Automatic XSRF token management and token‑based authentication for Jupyter servers.
• Robust error handling with fallback modes, logging, and resource cleanup.

Use cases of 🚀 Jupyter MCP Server

• Data visualization pipeline: generate and extract multi‑panel plots (histograms, box plots) using matplotlib/seaborn.
• Scientific computing: create 3D surface plots and complex visualizations, extract images for AI analysis.
• Automated notebook workflows: programmatically add, modify, and reorder markdown/code cells.
• Remote code execution and state persistence: maintain kernel state across AI agent sessions.
• Integrating AI assistants with existing Jupyter environments for real‑time data exploration.

FAQ from 🚀 Jupyter MCP Server

What image formats can be extracted?

PNG and JPEG images are supported. The server extracts base64‑encoded image data from matplotlib, seaborn, and plotly outputs.

What are the runtime requirements?

Python 3.11 or later, a running Jupyter Notebook server, and an MCP‑compatible client (e.g., Claude Desktop). Dependencies are listed in requirements.txt.

How does it handle authentication and security?

It automatically detects and manages XSRF tokens, supports token‑based Jupyter authentication, and implements graceful fallbacks for connection issues.

How does it compare to other Jupyter MCP servers?

This implementation offers 18 tools (vs. 10 or fewer in alternatives), supports both PNG and JPEG image extraction, provides automatic setup, works with all Jupyter versions, and uses WebSocket+HTTP for real‑time execution with automatic XSRF handling.

Where are notebooks stored?

All created notebooks are stored in the notebooks/ directory inside the project folder. The default notebook is mcp_notebook.ipynb.