🪐✨ Jupyter MCP Server
@datalayer
About 🪐✨ Jupyter MCP Server
🪐 🔧 Model Context Protocol (MCP) Server for Jupyter.
Config
No standard config provided
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RepositoryTools
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Overview
What is 🪐✨ Jupyter MCP Server?
An open-source MCP server that enables AI agents to connect to and manage Jupyter Notebooks in real-time. It bridges AI assistants with Jupyter environments, allowing direct notebook creation, cell execution, and output retrieval. Developed by Datalayer.
How to use 🪐✨ Jupyter MCP Server?
Install via pip install jupyter-mcp-server and configure your MCP client with environment variables JUPYTER_URL, JUPYTER_TOKEN, and ALLOW_IMG_OUTPUT. Run using uvx for quick start or Docker for production. Alternatively, run as a Jupyter Server extension. The server supports STDIO and Streamable HTTP transports.
Key features of 🪐✨ Jupyter MCP Server
- Real-time notebook control with instant output viewing.
- Smart execution that adjusts after cell failures.
- Context-aware understanding of entire notebooks.
- Multimodal output support (images, plots, text).
- Multi-notebook switching and management.
- JupyterLab integration with additional commands.
Use cases of 🪐✨ Jupyter MCP Server
- AI-assisted code development and debugging in Jupyter.
- Automated data analysis and visualization workflows.
- Interactive exploration of notebooks via natural language.
- Orchestrating multi-notebook experiments with an AI agent.
- Real-time collaborative editing with JupyterLab integration.
FAQ from 🪐✨ Jupyter MCP Server
What MCP clients are supported?
Works with any MCP client, including Claude Desktop, Cursor, Windsurf, and others.
How do I configure the server?
Set JUPYTER_URL, JUPYTER_TOKEN, and ALLOW_IMG_OUTPUT environment variables. A MCP_TOKEN is also required since version 1.0.0.
Is Docker supported?
Yes. Use the datalayer/jupyter-mcp-server Docker image with environment variables passed at runtime.
What are the runtime requirements?
Python environment with JupyterLab 4.4.1+, jupyter-collaboration, jupyter-mcp-tools, ipykernel, and pycrdt. Starting v1.0.2, datalayer_pycrdt is no longer needed.
Does it support JupyterHub or Google Colab?
Support for JupyterHub and Google Colab deployments is actively being developed; user feedback is welcome.
Frequently asked questions
What MCP clients are supported?
Works with any MCP client, including Claude Desktop, Cursor, Windsurf, and others.
How do I configure the server?
Set `JUPYTER_URL`, `JUPYTER_TOKEN`, and `ALLOW_IMG_OUTPUT` environment variables. A `MCP_TOKEN` is also required since version 1.0.0.
Is Docker supported?
Yes. Use the `datalayer/jupyter-mcp-server` Docker image with environment variables passed at runtime.
What are the runtime requirements?
Python environment with JupyterLab 4.4.1+, `jupyter-collaboration`, `jupyter-mcp-tools`, `ipykernel`, and `pycrdt`. Starting v1.0.2, `datalayer_pycrdt` is no longer needed.
Does it support JupyterHub or Google Colab?
Support for JupyterHub and Google Colab deployments is actively being developed; user feedback is welcome.
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