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.
@MCP-Mirror
基本信息
Mirror of
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"datalayer_jupyter-mcp-server": {
"command": "docker",
"args": [
"build",
"-t",
"datalayer/jupyter-mcp-server",
"."
]
}
}
}`cell_content`(string): Code to be executed
Success message
`cell_content`(string): Markdown content
Success message
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.
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.
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.
A local JupyterLab with `jupyter-collaboration` and `ipykernel` installed, plus Docker to run the MCP server container.
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.
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`.
The README only shows distribution via Docker or Smithery. There is no pip-based installation described.

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