An MCP server that lets AI agents connect to and manage Jupyter notebooks in real-time.
jupyter-mcp-server is worth checking the docs before setup with strong trust signals. Check agent compatibility and use-case fit before adding it to your workflow.
gh repo view datalayer/jupyter-mcp-server --webOpen the official README and confirm the supported install method.
Add the server entry to your MCP client config.
Restart your agent and verify that the server tools appear.
Repository setup guidance
Strong trust signals; still review the README and permissions before production use.
Last commit was about 120 days ago.
1141 GitHub stars indicate community interest.
37 open issues signal maintenance load.
BSD-3-Clause license detected.
3 security/trust notes recorded.
Setup difficulty is 3/5.
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This tool acts as a bridge between AI assistants and Jupyter notebooks. It allows an AI to view, edit, and run code in notebooks, seeing the results instantly. You can use it with any MCP-compatible client like Claude Desktop or Cursor.
Jupyter MCP Server is a Model Context Protocol (MCP) server developed by Datalayer that enables AI agents to interact with Jupyter notebooks in real-time. It provides a rich set of tools for notebook management, including creating, opening, editing, and executing cells, as well as viewing outputs such as text, images, and plots. The server supports multi-notebook workflows, smart execution that adapts to cell failures, and context-aware interactions that understand the entire notebook state. It integrates with JupyterLab for enhanced UI features and includes observability via OpenTelemetry. Compatible with any Jupyter deployment (local, JupyterHub, Google Colab) and works with any MCP client. The project is open-source under BSD-3-Clause license and has an active community.
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An MCP server that lets AI agents connect to and manage Jupyter notebooks in real-time.
Data scientists who want AI assistance in their Jupyter workflow., AI/ML engineers building agents that need to interact with computational notebooks., Developers creating MCP-based tools for scientific computing and data analysis.
The setup section provides repository-level starting guidance, not a guarantee of an independently verified installation. Check the official README and release notes.
The server requires an MCP token for authentication; ensure it is kept secret. Running arbitrary code from AI agents can be a security risk; use in a sandboxed environment if needed.
Similar or complementary options to evaluate include metorial, mcp-context-forge, Microsoft Learn MCP Server.
Aug 20, 2026