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.