What tools does Jupyter MCP Server offer?
Two tools: `add_execute_code_cell` (executes Python code and returns success) and `add_markdown_cell` (adds Markdown content). Both take a `cell_content` string.
Jupyter MCP Server
@MCP-Mirror
Basic information
About Jupyter MCP Server
Mirror of
Jupyter MCP Server config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"datalayer_jupyter-mcp-server": {
"command": "docker",
"args": [
"build",
"-t",
"datalayer/jupyter-mcp-server",
"."
]
}
}
}Jupyter MCP Server tools
4`cell_content`(string): Code to be executed
Success message
`cell_content`(string): Markdown content
Success message
Overview
What is Jupyter MCP Server?
Jupyter MCP Server is a Model Context Protocol (MCP) server that enables interaction with Jupyter notebooks running in a local JupyterLab instance. Designed for AI agents and developers, it uses Jupyter Real Time Collaboration (RTC) to reflect notebook modifications live.
How to use Jupyter MCP Server?
Start JupyterLab with token authentication (jupyter lab --port 8888 --IdentityProvider.token MY_TOKEN --ip 0.0.0.0) and install the required packages (jupyterlab, jupyter-collaboration, ipykernel). Then configure the MCP client (e.g., Claude Desktop) to run the Docker image datalayer/jupyter-mcp-server:latest, setting environment variables SERVER_URL, TOKEN, and NOTEBOOK_PATH to match your JupyterLab session.
Key features of Jupyter MCP Server
- Connect AI agents to live Jupyter notebooks
- Add and execute code cells remotely
- Insert Markdown cells into notebooks
- Uses Docker for easy deployment
- Works with JupyterLab’s Real Time Collaboration
Use cases of Jupyter MCP Server
- Enable a language model to run Python code in a Jupyter notebook
- Automatically generate and insert analysis cells with results
- Combine AI-generated explanations with computed outputs in a single notebook
Frequently asked questions
What are the runtime dependencies?
A local JupyterLab with `jupyter-collaboration` and `ipykernel` installed, plus Docker to run the MCP server container.
How do I authenticate to my JupyterLab instance?
You must set a token via `--IdentityProvider.token MY_TOKEN` when starting JupyterLab, then provide the same token in the `TOKEN` environment variable in the MCP client configuration.
How does the server connect to a running JupyterLab?
The `SERVER_URL` environment variable (e.g., `http://localhost:8888`) tells the Docker container where JupyterLab is listening. On Linux, the `--network=host` Docker flag is used; on macOS/Windows, the container accesses the host via `host.docker.internal`.
Can I install Jupyter MCP Server without Docker?
The README only shows distribution via Docker or Smithery. There is no pip-based installation described.
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