🪐🔧 Jupyter MCP Server

An MCP server developed for AI to connect and manage Jupyter Notebooks in real-time — and scale your Code Sandbox from local to the cloud (Datalayer, Kaggle, Google Colab, Modal, Daytona, E2B, CoreWeave, Cloudflare...)

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No process to run. Datalayer now hosts this server for you at https://mcp.datalayer.run/mcp — one endpoint for every agent and every notebook. Sign in from your browser, approve what the agent may do, and your work keeps running on the server after the agent disconnects.

→ Hosted Jupyter MCP Server

One command to connect Claude Code, with /datalayer:notebook, /datalayer:run and /datalayer:status on top:

/plugin marketplace add datalayer/jupyter-mcp-server
/plugin install datalayer

→ Datalayer plugin for Claude Code

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Free and open source, BSD 3-Clause — point it at any Jupyter you already run, local or JupyterHub, no account needed.

Built and maintained by Datalayer, where the same server drives always-on Notebooks with GPU Code Sandboxes and durable execution — so your agent keeps working on your data when your laptop does not.

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No token to copy and paste. An agent that meets this server unauthenticated is told where to authenticate, opens your browser, and you sign in to Datalayer as yourself. The agent never sees your password — it receives a token scoped to what you approved, and you can disconnect one agent without touching the others.

What each agent may do is two separate decisions: the scopes you approve (notebooks:read, notebooks:write, code:execute, data:read) say what kind of operation it may perform, and your own Datalayer permissions still say which notebooks it may touch. An agent can never reach a notebook you cannot.

Personal access tokens keep working, and remain the simpler path for a CLI or a script. → OAuth and identity

Pin code-sandboxes to match your jupyter-mcp-server. The sandbox variant jupyter was renamed to jupyter-server in code-sandboxes 1.1.1, and the two packages have to agree on the name.

Your jupyter-mcp-server	Install
>= 1.5.0	code-sandboxes >= 1.1.1
< 1.5.0	code-sandboxes <= 1.0.9

# On 1.5.0 or later
pip install "jupyter-mcp-server>=1.5.0" "code-sandboxes>=1.1.1"

# Staying on an earlier jupyter-mcp-server
pip install "jupyter-mcp-server<1.5.0" "code-sandboxes<=1.0.9"

An older server with a newer code-sandboxes installs cleanly and then fails on the first execution with Unknown sandbox variant: jupyter. → Release notes

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This is the beginning of the README. Read the rest at the original. Read the full README ↗