How do I install mcp-notebooks?
Clone the repository and run `docker build . -t mcp-notebooks:latest`. Then configure the server in `claude_desktop_config.json` as shown in the README.
@Neuron1c
基本情報
Notebook execution MCP server
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"mcp-notebooks": {
"command": "docker",
"args": [
"build",
".",
"-t",
"mcp-notebooks:latest"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
mcp-notebooks is an MCP server that enables an LLM to execute code progressively in a Jupyter-like kernel, retaining variables between executions for iterative exploratory data analysis (EDA). It is intended for users comfortable with Docker and willing to accept the current safety risks.
Build the Docker image from the repository, then add it to your claude_desktop_config.json either via StdIO (docker run) or SSE (docker run on port 3001 plus supergateway). Additional Python libraries can be installed with uv add <package> before building.
Clone the repository and run `docker build . -t mcp-notebooks:latest`. Then configure the server in `claude_desktop_config.json` as shown in the README.
Docker is required; the server is intended to be run inside a Docker container. It is not yet published on DockerHub.
Add libraries with `uv add your-package` before building the Docker image. Recommended packages include numpy, pandas, scikit‑learn, matplotlib, and seaborn.
The README explicitly warns that it is “pretty dangerous to use in its current state” and strongly recommends running it inside a Docker container for isolation.
The server supports both StdIO (through a direct Docker command) and SSE (via an HTTP endpoint on port 3001 using `supergateway`).
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