Jupyter MCP Server for Claude Desktop
@harshitha-8
About Jupyter MCP Server for Claude Desktop
Jupyter MCP Server Jupyter MCP Server is an implementation of the Model Context Protocol (MCP) server that enables interaction with Jupyter notebooks running in any JupyterLab environment, including your local JupyterLab instance
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"Integrating-the-Jupyter-server-with-claude-desktop-uisng-the-powerful-model-context-protocol": {
"command": "npx",
"args": [
"-y",
"@smithery/cli",
"install",
"@datalayer/jupyter-mcp-server",
"--client",
"claude"
]
}
}
}Tools
No tools detected
Fetch the live tool list by running this server in a temporary sandbox using the button above.
Overview
What is Jupyter MCP Server for Claude Desktop?
Jupyter MCP Server for Claude Desktop is an implementation of the Model Context Protocol (MCP) server that enables interaction with Jupyter notebooks running in any JupyterLab environment, including local instances. It is designed for seamless integration with Claude Desktop across macOS, Windows, and Linux.
How to use Jupyter MCP Server for Claude Desktop?
Install the server Docker image or use Smithery, configure it in Claude Desktop’s MCP settings with environment variables SERVER_URL, TOKEN, and NOTEBOOK_PATH, then start JupyterLab on port 8888. The server provides two tools: add_execute_code_cell and add_markdown_cell.
Key features of Jupyter MCP Server for Claude Desktop
- Provides an MCP server interface for Jupyter notebooks.
- Supports real-time collaboration via JupyterLab.
- Integrates smoothly with Claude Desktop on all platforms.
- Programmatically add and execute code or markdown cells.
- Runs inside Docker for easy deployment.
Use cases of Jupyter MCP Server for Claude Desktop
- Let Claude interactively execute Python code in a Jupyter notebook.
- Automate notebook cell creation and execution from AI conversations.
- Enable collaborative data analysis with AI assistance.
- Integrate JupyterLab workflows with Claude Desktop’s MCP capabilities.
FAQ from Jupyter MCP Server for Claude Desktop
What dependencies are required to run the server?
You need JupyterLab 4.4.1, jupyter-collaboration 4.0.2, ipykernel, and datalayer_pycrdt 0.12.15. The server itself runs in Docker.
How do I configure the server for Linux vs macOS/Windows?
On Linux use --network=host and http://localhost:8888; on macOS/Windows use http://host.docker.internal:8888. The TOKEN must match your JupyterLab setup.
Can I install the server without Docker?
The README only describes Docker usage and Smithery installation via npx. No native install is documented.
What tools does the server expose?
Two tools: add_execute_code_cell (takes cell_content string, returns cell output) and add_markdown_cell (takes cell_content string, returns success message).
Where does the notebook file live?
The NOTEBOOK_PATH environment variable defines the notebook file name (e.g., notebook.ipynb), relative to the directory where JupyterLab is started.
Frequently asked questions
What dependencies are required to run the server?
You need JupyterLab 4.4.1, jupyter-collaboration 4.0.2, ipykernel, and datalayer_pycrdt 0.12.15. The server itself runs in Docker.
How do I configure the server for Linux vs macOS/Windows?
On Linux use `--network=host` and `http://localhost:8888`; on macOS/Windows use `http://host.docker.internal:8888`. The `TOKEN` must match your JupyterLab setup.
Can I install the server without Docker?
The README only describes Docker usage and Smithery installation via `npx`. No native install is documented.
What tools does the server expose?
Two tools: `add_execute_code_cell` (takes `cell_content` string, returns cell output) and `add_markdown_cell` (takes `cell_content` string, returns success message).
Where does the notebook file live?
The `NOTEBOOK_PATH` environment variable defines the notebook file name (e.g., `notebook.ipynb`), relative to the directory where JupyterLab is started.
Basic information
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