[{"_1":2,"_377":-5,"_378":-5},"loaderData",{"_3":4,"_22":23,"_278":279},"root",{"_5":6,"_7":8},"toasts",[],"visitor",{"_9":10,"_11":12},"viewportWidth",1280,"visitorSession",{"_13":14,"_16":17,"_18":-5,"_19":20,"_21":-5},"attributes",[15],"bot","id",14674407,"membership","referenceId","0199368b-156e-7599-a55d-ca69a5c1bb1e","userAccount","routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_24":25,"_79":-5,"_80":38,"_27":81,"_194":195,"_277":175},"availableLocales",[26,38,44,51,58,65,72],{"_27":28,"_31":35,"_36":37},"mcpServer",{"_29":30,"_31":32,"_33":34},"description","Ermöglicht die Interaktion mit Jupyter-Notebooks über das Model Context Protocol und unterstützt die Codeausführung und das Einfügen von Markdowns in JupyterLab-Umgebungen.","name","Jupyter MCP Server","readme","[![Datenschicht](https://assets.datalayer.tech/datalayer-25.svg)](https://datalayer.io)\n\n[![Werden Sie Sponsor](https://img.shields.io/static/v1?label=Become%20a%20Sponsor\\&message=%E2%9D%A4\\&logo=GitHub\\&style=flat\\&color=1ABC9C)](https://github.com/sponsors/datalayer)\n\n# 🪐 ✨ Jupyter MCP Server\n\n[![Github-Aktionsstatus](https://github.com/datalayer/jupyter-mcp-server/workflows/Build/badge.svg)](https://github.com/datalayer/jupyter-mcp-server/actions/workflows/build.yml) [![PyPI - Version](https://img.shields.io/pypi/v/jupyter-mcp-server)](https://pypi.org/project/jupyter-mcp-server) [![Schmiedeabzeichen](https://smithery.ai/badge/@datalayer/jupyter-mcp-server)](https://smithery.ai/server/@datalayer/jupyter-mcp-server)\n\nJupyter MCP Server ist eine Serverimplementierung [des Model Context Protocol](https://modelcontextprotocol.io) (MCP), die die Interaktion mit 📓 Jupyter-Notebooks ermöglicht, die in jedem JupyterLab ausgeführt werden (funktioniert auch mit Ihrem 💻 lokalen JupyterLab).\n\n![Jupyter MCP Server](https://assets.datalayer.tech/jupyter-mcp/jupyter-mcp-server-claude-demo.gif)\n\n## Starten Sie JupyterLab\n\nStellen Sie sicher, dass Sie Folgendes installiert haben. Das Collaboration-Paket wird benötigt, da die am Notebook vorgenommenen Änderungen dank [Jupyter Real Time Collaboration](https://jupyterlab.readthedocs.io/en/stable/user/rtc.html) sichtbar sind.\n\n```bash\npip install jupyterlab==4.4.1 jupyter-collaboration==4.0.2 ipykernel\npip uninstall -y pycrdt datalayer_pycrdt\npip install datalayer_pycrdt==0.12.17\n```\n\nStarten Sie dann JupyterLab mit dem folgenden Befehl.\n\n```bash\njupyter lab --port 8888 --IdentityProvider.token MY_TOKEN --ip 0.0.0.0\n```\n\nSie können auch `make jupyterlab` ausführen.\n\n> \\[!NOTIZ]\n>\n> Die `--ip` wird auf `0.0.0.0` gesetzt, um dem in einem Docker-Container ausgeführten MCP-Server den Zugriff auf Ihr lokales JupyterLab zu ermöglichen.\n\n## Verwendung mit Claude Desktop\n\nClaude Desktop kann [von dieser Seite](https://claude.ai/download) für macOS und Windows heruntergeladen werden.\n\nFür Linux hatten wir Erfolg mit diesem [inoffiziellen Build-Skript basierend auf nix](https://github.com/k3d3/claude-desktop-linux-flake)\n\n```bash\n# ⚠️ UNOFFICIAL\n# You can also run `make claude-linux`\nNIXPKGS_ALLOW_UNFREE=1 nix run github:k3d3/claude-desktop-linux-flake \\\n --impure \\\n --extra-experimental-features flakes \\\n --extra-experimental-features nix-command\n```\n\nUm dies mit Claude Desktop zu verwenden, fügen Sie Folgendes zu Ihrer `claude_desktop_config.json` hinzu (lesen Sie mehr auf der [MCP-Dokumentationswebsite](https://modelcontextprotocol.io/quickstart/user#2-add-the-filesystem-mcp-server) ).\n\n> \\[!WICHTIG]\n>\n> Stellen Sie sicher, dass der Port der `SERVER_URL` und `TOKEN` mit denen im `jupyter lab` -Befehl übereinstimmen.\n>\n> Der `NOTEBOOK_PATH` sollte relativ zum Verzeichnis sein, in dem JupyterLab gestartet wurde.\n\n### Claude-Konfiguration unter macOS und Windows\n\n```json\n{\n \"mcpServers\": {\n \"jupyter\": {\n \"command\": \"docker\",\n \"args\": [\n \"run\",\n \"-i\",\n \"--rm\",\n \"-e\",\n \"SERVER_URL\",\n \"-e\",\n \"TOKEN\",\n \"-e\",\n \"NOTEBOOK_PATH\",\n \"datalayer/jupyter-mcp-server:latest\"\n ],\n \"env\": {\n \"SERVER_URL\": \"http://host.docker.internal:8888\",\n \"TOKEN\": \"MY_TOKEN\",\n \"NOTEBOOK_PATH\": \"notebook.ipynb\"\n }\n }\n }\n}\n```\n\n### Claude-Konfiguration unter Linux\n\n```bash\nCLAUDE_CONFIG=${HOME}/.config/Claude/claude_desktop_config.json\ncat < $CLAUDE_CONFIG\n{\n \"mcpServers\": {\n \"jupyter\": {\n \"command\": \"docker\",\n \"args\": [\n \"run\",\n \"-i\",\n \"--rm\",\n \"-e\",\n \"SERVER_URL\",\n \"-e\",\n \"TOKEN\",\n \"-e\",\n \"NOTEBOOK_PATH\",\n \"--network=host\",\n \"datalayer/jupyter-mcp-server:latest\"\n ],\n \"env\": {\n \"SERVER_URL\": \"http://localhost:8888\",\n \"TOKEN\": \"MY_TOKEN\",\n \"NOTEBOOK_PATH\": \"notebook.ipynb\"\n }\n }\n }\n}\nEOF\ncat $CLAUDE_CONFIG\n```\n\n## Komponenten\n\n### Werkzeuge\n\nDer Server bietet derzeit 2 Tools:\n\n1. `add_execute_code_cell`\n\n* Fügen Sie eine Codezelle in einem Jupyter-Notebook hinzu und führen Sie sie aus.\n* Eingang:\n * `cell_content` (Zeichenfolge): Auszuführender Code.\n* Rückgabe: Zellenausgabe.\n\n2. `add_markdown_cell`\n\n* Fügen Sie eine Markdown-Zelle in einem Jupyter-Notizbuch hinzu.\n* Eingang:\n * `cell_content` (Zeichenfolge): Markdown-Inhalt.\n* Gibt zurück: Erfolgsmeldung.\n\n## Gebäude\n\nSie können das Docker-Image aus der Quelle erstellen.\n\n```bash\nmake build-docker\n```\n\n## Installation über Smithery\n\nSo installieren Sie den Jupyter MCP Server für Claude Desktop automatisch über [Smithery](https://smithery.ai/server/@datalayer/jupyter-mcp-server) :\n\n```bash\nnpx -y @smithery/cli install @datalayer/jupyter-mcp-server --client claude\n```","German","tag","de-DE",{"_27":39,"_31":42,"_36":43},{"_29":40,"_31":32,"_33":41},"Enables interaction with Jupyter notebooks through the Model Context Protocol, supporting code execution and markdown insertion within JupyterLab environments.","\n\n[![Datalayer](https://assets.datalayer.tech/datalayer-25.svg)](https://datalayer.io)\n\n[![Become a Sponsor](https://img.shields.io/static/v1?label=Become%20a%20Sponsor&message=%E2%9D%A4&logo=GitHub&style=flat&color=1ABC9C)](https://github.com/sponsors/datalayer)\n\n# 🪐 ✨ Jupyter MCP Server\n\n[![Github Actions Status](https://github.com/datalayer/jupyter-mcp-server/workflows/Build/badge.svg)](https://github.com/datalayer/jupyter-mcp-server/actions/workflows/build.yml)\n[![PyPI - Version](https://img.shields.io/pypi/v/jupyter-mcp-server)](https://pypi.org/project/jupyter-mcp-server)\n[![smithery badge](https://smithery.ai/badge/@datalayer/jupyter-mcp-server)](https://smithery.ai/server/@datalayer/jupyter-mcp-server)\n\nJupyter MCP Server is a [Model Context Protocol](https://modelcontextprotocol.io) (MCP) server implementation that provides interaction with 📓 Jupyter notebooks running in any JupyterLab (works also with your 💻 local JupyterLab).\n\n![Jupyter MCP Server](https://assets.datalayer.tech/jupyter-mcp/jupyter-mcp-server-claude-demo.gif)\n\n## Start JupyterLab\n\nMake sure you have the following installed. The collaboration package is needed as the modifications made on the notebook can be seen thanks to [Jupyter Real Time Collaboration](https://jupyterlab.readthedocs.io/en/stable/user/rtc.html).\n\n```bash\npip install jupyterlab==4.4.1 jupyter-collaboration==4.0.2 ipykernel\npip uninstall -y pycrdt datalayer_pycrdt\npip install datalayer_pycrdt==0.12.17\n```\n\nThen, start JupyterLab with the following command.\n\n```bash\njupyter lab --port 8888 --IdentityProvider.token MY_TOKEN --ip 0.0.0.0\n```\n\nYou can also run `make jupyterlab`.\n\n> [!NOTE]\n>\n> The `--ip` is set to `0.0.0.0` to allow the MCP server running in a Docker container to access your local JupyterLab.\n\n## Use with Claude Desktop\n\nClaude Desktop can be downloaded [from this page](https://claude.ai/download) for macOS and Windows.\n\nFor Linux, we had success using this [UNOFFICIAL build script based on nix](https://github.com/k3d3/claude-desktop-linux-flake)\n\n```bash\n# ⚠️ UNOFFICIAL\n# You can also run `make claude-linux`\nNIXPKGS_ALLOW_UNFREE=1 nix run github:k3d3/claude-desktop-linux-flake \\\n --impure \\\n --extra-experimental-features flakes \\\n --extra-experimental-features nix-command\n```\n\nTo use this with Claude Desktop, add the following to your `claude_desktop_config.json` (read more on the [MCP documentation website](https://modelcontextprotocol.io/quickstart/user#2-add-the-filesystem-mcp-server)).\n\n> [!IMPORTANT]\n>\n> Ensure the port of the `SERVER_URL`and `TOKEN` match those used in the `jupyter lab` command.\n>\n> The `NOTEBOOK_PATH` should be relative to the directory where JupyterLab was started.\n\n### Claude Configuration on macOS and Windows\n\n```json\n{\n \"mcpServers\": {\n \"jupyter\": {\n \"command\": \"docker\",\n \"args\": [\n \"run\",\n \"-i\",\n \"--rm\",\n \"-e\",\n \"SERVER_URL\",\n \"-e\",\n \"TOKEN\",\n \"-e\",\n \"NOTEBOOK_PATH\",\n \"datalayer/jupyter-mcp-server:latest\"\n ],\n \"env\": {\n \"SERVER_URL\": \"http://host.docker.internal:8888\",\n \"TOKEN\": \"MY_TOKEN\",\n \"NOTEBOOK_PATH\": \"notebook.ipynb\"\n }\n }\n }\n}\n```\n\n### Claude Configuration on Linux\n\n```bash\nCLAUDE_CONFIG=${HOME}/.config/Claude/claude_desktop_config.json\ncat < $CLAUDE_CONFIG\n{\n \"mcpServers\": {\n \"jupyter\": {\n \"command\": \"docker\",\n \"args\": [\n \"run\",\n \"-i\",\n \"--rm\",\n \"-e\",\n \"SERVER_URL\",\n \"-e\",\n \"TOKEN\",\n \"-e\",\n \"NOTEBOOK_PATH\",\n \"--network=host\",\n \"datalayer/jupyter-mcp-server:latest\"\n ],\n \"env\": {\n \"SERVER_URL\": \"http://localhost:8888\",\n \"TOKEN\": \"MY_TOKEN\",\n \"NOTEBOOK_PATH\": \"notebook.ipynb\"\n }\n }\n }\n}\nEOF\ncat $CLAUDE_CONFIG\n```\n\n## Components\n\n### Tools\n\nThe server currently offers 2 tools:\n\n1. `add_execute_code_cell`\n\n- Add and execute a code cell in a Jupyter notebook.\n- Input:\n - `cell_content`(string): Code to be executed.\n- Returns: Cell output.\n\n2. `add_markdown_cell`\n\n- Add a markdown cell in a Jupyter notebook.\n- Input:\n - `cell_content`(string): Markdown content.\n- Returns: Success message.\n\n## Building\n\nYou can build the Docker image it from source.\n\n```bash\nmake build-docker\n```\n\n## Installing via Smithery\n\nTo install Jupyter MCP Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@datalayer/jupyter-mcp-server):\n\n```bash\nnpx -y @smithery/cli install @datalayer/jupyter-mcp-server --client claude\n```\n","English","en-US",{"_27":45,"_31":49,"_36":50},{"_29":46,"_31":47,"_33":48},"Permite la interacción con cuadernos Jupyter a través del Protocolo de contexto de modelo, lo que admite la ejecución de código y la inserción de rebajas dentro de entornos JupyterLab.","Servidor Jupyter MCP","[![Capa de datos](https://assets.datalayer.tech/datalayer-25.svg)](https://datalayer.io)\n\n[![Conviértete en patrocinador](https://img.shields.io/static/v1?label=Become%20a%20Sponsor\\&message=%E2%9D%A4\\&logo=GitHub\\&style=flat\\&color=1ABC9C)](https://github.com/sponsors/datalayer)\n\n# 🪐 ✨ Servidor Jupyter MCP\n\n[![Estado de las acciones de Github](https://github.com/datalayer/jupyter-mcp-server/workflows/Build/badge.svg)](https://github.com/datalayer/jupyter-mcp-server/actions/workflows/build.yml) [![PyPI - Versión](https://img.shields.io/pypi/v/jupyter-mcp-server)](https://pypi.org/project/jupyter-mcp-server) [![insignia de herrería](https://smithery.ai/badge/@datalayer/jupyter-mcp-server)](https://smithery.ai/server/@datalayer/jupyter-mcp-server)\n\nJupyter MCP Server es una implementación de servidor [de Protocolo de contexto de modelo](https://modelcontextprotocol.io) (MCP) que proporciona interacción con 📓 cuadernos Jupyter que se ejecutan en cualquier JupyterLab (también funciona con su 💻 JupyterLab local).\n\n![Servidor Jupyter MCP](https://assets.datalayer.tech/jupyter-mcp/jupyter-mcp-server-claude-demo.gif)\n\n## Iniciar JupyterLab\n\nAsegúrese de tener instalado lo siguiente. El paquete de colaboración es necesario, ya que las modificaciones realizadas en el notebook se pueden ver gracias a [Jupyter Real Time Collaboration](https://jupyterlab.readthedocs.io/en/stable/user/rtc.html) .\n\n```bash\npip install jupyterlab==4.4.1 jupyter-collaboration==4.0.2 ipykernel\npip uninstall -y pycrdt datalayer_pycrdt\npip install datalayer_pycrdt==0.12.17\n```\n\nLuego, inicie JupyterLab con el siguiente comando.\n\n```bash\njupyter lab --port 8888 --IdentityProvider.token MY_TOKEN --ip 0.0.0.0\n```\n\nTambién puedes ejecutar `make jupyterlab` .\n\n> \\[!NOTA]\n>\n> La `--ip` se establece en `0.0.0.0` para permitir que el servidor MCP que se ejecuta en un contenedor Docker acceda a su JupyterLab local.\n\n## Usar con Claude Desktop\n\nClaude Desktop se puede descargar [desde esta página](https://claude.ai/download) para macOS y Windows.\n\nPara Linux, tuvimos éxito al usar este [script de compilación NO OFICIAL basado en nix](https://github.com/k3d3/claude-desktop-linux-flake)\n\n```bash\n# ⚠️ UNOFFICIAL\n# You can also run `make claude-linux`\nNIXPKGS_ALLOW_UNFREE=1 nix run github:k3d3/claude-desktop-linux-flake \\\n --impure \\\n --extra-experimental-features flakes \\\n --extra-experimental-features nix-command\n```\n\nPara usar esto con Claude Desktop, agregue lo siguiente a su `claude_desktop_config.json` (lea más en el [sitio web de documentación de MCP](https://modelcontextprotocol.io/quickstart/user#2-add-the-filesystem-mcp-server) ).\n\n> \\[!IMPORTANTE]\n>\n> Asegúrese de que el puerto de `SERVER_URL` y `TOKEN` coincidan con los utilizados en el comando `jupyter lab` .\n>\n> `NOTEBOOK_PATH` debe ser relativo al directorio donde se inició JupyterLab.\n\n### Configuración de Claude en macOS y Windows\n\n```json\n{\n \"mcpServers\": {\n \"jupyter\": {\n \"command\": \"docker\",\n \"args\": [\n \"run\",\n \"-i\",\n \"--rm\",\n \"-e\",\n \"SERVER_URL\",\n \"-e\",\n \"TOKEN\",\n \"-e\",\n \"NOTEBOOK_PATH\",\n \"datalayer/jupyter-mcp-server:latest\"\n ],\n \"env\": {\n \"SERVER_URL\": \"http://host.docker.internal:8888\",\n \"TOKEN\": \"MY_TOKEN\",\n \"NOTEBOOK_PATH\": \"notebook.ipynb\"\n }\n }\n }\n}\n```\n\n### Configuración de Claude en Linux\n\n```bash\nCLAUDE_CONFIG=${HOME}/.config/Claude/claude_desktop_config.json\ncat < $CLAUDE_CONFIG\n{\n \"mcpServers\": {\n \"jupyter\": {\n \"command\": \"docker\",\n \"args\": [\n \"run\",\n \"-i\",\n \"--rm\",\n \"-e\",\n \"SERVER_URL\",\n \"-e\",\n \"TOKEN\",\n \"-e\",\n \"NOTEBOOK_PATH\",\n \"--network=host\",\n \"datalayer/jupyter-mcp-server:latest\"\n ],\n \"env\": {\n \"SERVER_URL\": \"http://localhost:8888\",\n \"TOKEN\": \"MY_TOKEN\",\n \"NOTEBOOK_PATH\": \"notebook.ipynb\"\n }\n }\n }\n}\nEOF\ncat $CLAUDE_CONFIG\n```\n\n## Componentes\n\n### Herramientas\n\nEl servidor actualmente ofrece 2 herramientas:\n\n1. `add_execute_code_cell`\n\n* Agregar y ejecutar una celda de código en un cuaderno Jupyter.\n* Aporte:\n * `cell_content` (cadena): Código a ejecutar.\n* Devuelve: Salida de la celda.\n\n2. `add_markdown_cell`\n\n* Agregue una celda de rebajas en un cuaderno Jupyter.\n* Aporte:\n * `cell_content` (cadena): contenido Markdown.\n* Devuelve: Mensaje de éxito.\n\n## Edificio\n\nPuedes crear la imagen de Docker desde la fuente.\n\n```bash\nmake build-docker\n```\n\n## Instalación mediante herrería\n\nPara instalar Jupyter MCP Server para Claude Desktop automáticamente a través de [Smithery](https://smithery.ai/server/@datalayer/jupyter-mcp-server) :\n\n```bash\nnpx -y @smithery/cli install @datalayer/jupyter-mcp-server --client claude\n```","Spanish","es-ES",{"_27":52,"_31":56,"_36":57},{"_29":53,"_31":54,"_33":55},"モデルコンテキストプロトコルを介して Jupyter ノートブックとのやり取りを可能にし、JupyterLab 環境内でのコード実行とマークダウン挿入をサポートします。","Jupyter MCP サーバー","[![データレイヤー](https://assets.datalayer.tech/datalayer-25.svg)](https://datalayer.io)\n\n[![スポンサーになる](https://img.shields.io/static/v1?label=Become%20a%20Sponsor\\&message=%E2%9D%A4\\&logo=GitHub\\&style=flat\\&color=1ABC9C)](https://github.com/sponsors/datalayer)\n\n# 🪐 ✨ Jupyter MCP サーバー\n\n[![Githubアクションステータス](https://github.com/datalayer/jupyter-mcp-server/workflows/Build/badge.svg)](https://github.com/datalayer/jupyter-mcp-server/actions/workflows/build.yml) [![PyPI - バージョン](https://img.shields.io/pypi/v/jupyter-mcp-server)](https://pypi.org/project/jupyter-mcp-server) [![鍛冶屋のバッジ](https://smithery.ai/badge/@datalayer/jupyter-mcp-server)](https://smithery.ai/server/@datalayer/jupyter-mcp-server)\n\nJupyter MCP サーバーは、任意の JupyterLab で実行されている 📓 Jupyter ノートブックとのやり取りを提供する[モデルコンテキストプロトコル](https://modelcontextprotocol.io)(MCP) サーバー実装です (💻 ローカル JupyterLab でも動作します)。\n\n![Jupyter MCP サーバー](https://assets.datalayer.tech/jupyter-mcp/jupyter-mcp-server-claude-demo.gif)\n\n## JupyterLabを起動する\n\n以下のパッケージがインストールされていることを確認してください。Jupyter [Real Time Collaboration](https://jupyterlab.readthedocs.io/en/stable/user/rtc.html)により、ノートブックに加えられた変更内容を確認できるため、コラボレーションパッケージが必要です。\n\n```bash\npip install jupyterlab==4.4.1 jupyter-collaboration==4.0.2 ipykernel\npip uninstall -y pycrdt datalayer_pycrdt\npip install datalayer_pycrdt==0.12.17\n```\n\n次に、以下のコマンドでJupyterLabを起動します。\n\n```bash\njupyter lab --port 8888 --IdentityProvider.token MY_TOKEN --ip 0.0.0.0\n```\n\n`make jupyterlab`を実行することもできます。\n\n> \\[！注記]\n>\n> `--ip`は`0.0.0.0`に設定され、Docker コンテナで実行されている MCP サーバーがローカルの JupyterLab にアクセスできるようになります。\n\n## Claude Desktop で使用する\n\nClaude Desktop は、macOS および Windows 向けに[このページから](https://claude.ai/download)ダウンロードできます。\n\nLinuxでは、 [nixベースのこの非公式ビルドスクリプト](https://github.com/k3d3/claude-desktop-linux-flake)を使用することで成功しました。\n\n```bash\n# ⚠️ UNOFFICIAL\n# You can also run `make claude-linux`\nNIXPKGS_ALLOW_UNFREE=1 nix run github:k3d3/claude-desktop-linux-flake \\\n --impure \\\n --extra-experimental-features flakes \\\n --extra-experimental-features nix-command\n```\n\nこれを Claude Desktop で使用するには、 `claude_desktop_config.json`に次のコードを追加します (詳細については、 [MCP ドキュメント Web サイト](https://modelcontextprotocol.io/quickstart/user#2-add-the-filesystem-mcp-server)を参照してください)。\n\n> \\[！重要]\n>\n> `SERVER_URL`と`TOKEN`のポートが、 `jupyter lab`コマンドで使用されているポートと一致していることを確認します。\n>\n> `NOTEBOOK_PATH`は、JupyterLab が起動されたディレクトリを基準にする必要があります。\n\n### macOSとWindowsでのClaudeの構成\n\n```json\n{\n \"mcpServers\": {\n \"jupyter\": {\n \"command\": \"docker\",\n \"args\": [\n \"run\",\n \"-i\",\n \"--rm\",\n \"-e\",\n \"SERVER_URL\",\n \"-e\",\n \"TOKEN\",\n \"-e\",\n \"NOTEBOOK_PATH\",\n \"datalayer/jupyter-mcp-server:latest\"\n ],\n \"env\": {\n \"SERVER_URL\": \"http://host.docker.internal:8888\",\n \"TOKEN\": \"MY_TOKEN\",\n \"NOTEBOOK_PATH\": \"notebook.ipynb\"\n }\n }\n }\n}\n```\n\n### LinuxでのClaudeの設定\n\n```bash\nCLAUDE_CONFIG=${HOME}/.config/Claude/claude_desktop_config.json\ncat < $CLAUDE_CONFIG\n{\n \"mcpServers\": {\n \"jupyter\": {\n \"command\": \"docker\",\n \"args\": [\n \"run\",\n \"-i\",\n \"--rm\",\n \"-e\",\n \"SERVER_URL\",\n \"-e\",\n \"TOKEN\",\n \"-e\",\n \"NOTEBOOK_PATH\",\n \"--network=host\",\n \"datalayer/jupyter-mcp-server:latest\"\n ],\n \"env\": {\n \"SERVER_URL\": \"http://localhost:8888\",\n \"TOKEN\": \"MY_TOKEN\",\n \"NOTEBOOK_PATH\": \"notebook.ipynb\"\n }\n }\n }\n}\nEOF\ncat $CLAUDE_CONFIG\n```\n\n## コンポーネント\n\n### ツール\n\nサーバーは現在 2 つのツールを提供しています:\n\n1. `add_execute_code_cell`\n\n* Jupyter ノートブックにコードセルを追加して実行します。\n* 入力：\n * `cell_content` (文字列): 実行されるコード。\n* 戻り値: セル出力。\n\n2. `add_markdown_cell`\n\n* Jupyter ノートブックにマークダウンセルを追加します。\n* 入力：\n * `cell_content` (文字列): Markdown コンテンツ。\n* 戻り値: 成功メッセージ。\n\n## 建物\n\nDocker イメージをソースからビルドできます。\n\n```bash\nmake build-docker\n```\n\n## Smithery経由でインストール\n\n[Smithery](https://smithery.ai/server/@datalayer/jupyter-mcp-server)経由で Claude Desktop 用の Jupyter MCP サーバーを自動的にインストールするには:\n\n```bash\nnpx -y @smithery/cli install @datalayer/jupyter-mcp-server --client claude\n```","Japanese","ja-JP",{"_27":59,"_31":63,"_36":64},{"_29":60,"_31":61,"_33":62},"모델 컨텍스트 프로토콜을 통해 Jupyter 노트북과의 상호작용을 지원하여 JupyterLab 환경 내에서 코드 실행과 마크다운 삽입을 지원합니다.","Jupyter MCP 서버","[![데이터레이어](https://assets.datalayer.tech/datalayer-25.svg)](https://datalayer.io)\n\n[![스폰서가 되세요](https://img.shields.io/static/v1?label=Become%20a%20Sponsor\\&message=%E2%9D%A4\\&logo=GitHub\\&style=flat\\&color=1ABC9C)](https://github.com/sponsors/datalayer)\n\n# 🪐 ✨ Jupyter MCP 서버\n\n[![Github 작업 상태](https://github.com/datalayer/jupyter-mcp-server/workflows/Build/badge.svg)](https://github.com/datalayer/jupyter-mcp-server/actions/workflows/build.yml) [![PyPI - 버전](https://img.shields.io/pypi/v/jupyter-mcp-server)](https://pypi.org/project/jupyter-mcp-server) [![대장간 배지](https://smithery.ai/badge/@datalayer/jupyter-mcp-server)](https://smithery.ai/server/@datalayer/jupyter-mcp-server)\n\nJupyter MCP 서버는 JupyterLab에서 실행되는 📓 Jupyter 노트북과의 상호작용을 제공하는 MCP( [Model Context Protocol](https://modelcontextprotocol.io) ) 서버 구현입니다(로컬 JupyterLab에서도 작동).\n\n![Jupyter MCP 서버](https://assets.datalayer.tech/jupyter-mcp/jupyter-mcp-server-claude-demo.gif)\n\n## JupyterLab 시작하기\n\n다음이 설치되어 있는지 확인하세요. [Jupyter Real Time Collaboration을](https://jupyterlab.readthedocs.io/en/stable/user/rtc.html) 통해 노트북에서 수정한 내용을 확인할 수 있으므로 협업 패키지가 필요합니다.\n\n지엑스피1\n\n그런 다음, 다음 명령으로 JupyterLab을 시작합니다.\n\n```bash\njupyter lab --port 8888 --IdentityProvider.token MY_TOKEN --ip 0.0.0.0\n```\n\n`make jupyterlab` 실행할 수도 있습니다.\n\n> \\[!메모]\n>\n> `--ip` `0.0.0.0` 으로 설정하면 Docker 컨테이너에서 실행되는 MCP 서버가 로컬 JupyterLab에 액세스할 수 있습니다.\n\n## Claude Desktop과 함께 사용\n\nClaude Desktop은 [이 페이지에서](https://claude.ai/download) macOS 및 Windows용으로 다운로드할 수 있습니다.\n\nLinux의 경우 [nix 기반의 이 비공식 빌드 스크립트를](https://github.com/k3d3/claude-desktop-linux-flake) 사용하여 성공했습니다.\n\n```bash\n# ⚠️ UNOFFICIAL\n# You can also run `make claude-linux`\nNIXPKGS_ALLOW_UNFREE=1 nix run github:k3d3/claude-desktop-linux-flake \\\n --impure \\\n --extra-experimental-features flakes \\\n --extra-experimental-features nix-command\n```\n\nClaude Desktop과 함께 사용하려면 다음을 `claude_desktop_config.json` 에 추가하세요(자세한 내용은 [MCP 문서 웹사이트](https://modelcontextprotocol.io/quickstart/user#2-add-the-filesystem-mcp-server) 에서 확인하세요).\n\n> \\[!중요한]\n>\n> `SERVER_URL` 과 `TOKEN` 의 포트가 `jupyter lab` 명령에서 사용된 포트와 일치하는지 확인하세요.\n>\n> `NOTEBOOK_PATH` 는 JupyterLab이 시작된 디렉토리를 기준으로 해야 합니다.\n\n### macOS 및 Windows에서의 Claude 구성\n\n```json\n{\n \"mcpServers\": {\n \"jupyter\": {\n \"command\": \"docker\",\n \"args\": [\n \"run\",\n \"-i\",\n \"--rm\",\n \"-e\",\n \"SERVER_URL\",\n \"-e\",\n \"TOKEN\",\n \"-e\",\n \"NOTEBOOK_PATH\",\n \"datalayer/jupyter-mcp-server:latest\"\n ],\n \"env\": {\n \"SERVER_URL\": \"http://host.docker.internal:8888\",\n \"TOKEN\": \"MY_TOKEN\",\n \"NOTEBOOK_PATH\": \"notebook.ipynb\"\n }\n }\n }\n}\n```\n\n### Linux에서의 Claude 구성\n\n```bash\nCLAUDE_CONFIG=${HOME}/.config/Claude/claude_desktop_config.json\ncat < $CLAUDE_CONFIG\n{\n \"mcpServers\": {\n \"jupyter\": {\n \"command\": \"docker\",\n \"args\": [\n \"run\",\n \"-i\",\n \"--rm\",\n \"-e\",\n \"SERVER_URL\",\n \"-e\",\n \"TOKEN\",\n \"-e\",\n \"NOTEBOOK_PATH\",\n \"--network=host\",\n \"datalayer/jupyter-mcp-server:latest\"\n ],\n \"env\": {\n \"SERVER_URL\": \"http://localhost:8888\",\n \"TOKEN\": \"MY_TOKEN\",\n \"NOTEBOOK_PATH\": \"notebook.ipynb\"\n }\n }\n }\n}\nEOF\ncat $CLAUDE_CONFIG\n```\n\n## 구성 요소\n\n### 도구\n\n현재 서버는 2가지 도구를 제공합니다.\n\n1. `add_execute_code_cell`\n\n* Jupyter Notebook에 코드 셀을 추가하고 실행합니다.\n* 입력:\n * `cell_content` (문자열): 실행할 코드.\n* 반환: 셀 출력.\n\n2. `add_markdown_cell`\n\n* Jupyter Notebook에 마크다운 셀을 추가합니다.\n* 입력:\n * `cell_content` (문자열): 마크다운 콘텐츠.\n* 반환: 성공 메시지.\n\n## 건물\n\n소스에서 Docker 이미지를 빌드할 수 있습니다.\n\n```bash\nmake build-docker\n```\n\n## Smithery를 통해 설치\n\n[Smithery를](https://smithery.ai/server/@datalayer/jupyter-mcp-server) 통해 Claude Desktop에 Jupyter MCP 서버를 자동으로 설치하려면:\n\n```bash\nnpx -y @smithery/cli install @datalayer/jupyter-mcp-server --client claude\n```","Korean","ko-KR",{"_27":66,"_31":70,"_36":71},{"_29":67,"_31":68,"_33":69},"Обеспечивает взаимодействие с блокнотами Jupyter через протокол контекста модели, поддерживая выполнение кода и вставку разметки в средах JupyterLab.","Сервер Jupyter MCP","[![Datalayer](https://assets.datalayer.tech/datalayer-25.svg)](https://datalayer.io)\n\n[![Стать спонсором](https://img.shields.io/static/v1?label=Become%20a%20Sponsor\\&message=%E2%9D%A4\\&logo=GitHub\\&style=flat\\&color=1ABC9C)](https://github.com/sponsors/datalayer)\n\n# 🪐 ✨ Jupyter MCP-сервер\n\n[![Статус действий Github](https://github.com/datalayer/jupyter-mcp-server/workflows/Build/badge.svg)](https://github.com/datalayer/jupyter-mcp-server/actions/workflows/build.yml) [![PyPI - Версия](https://img.shields.io/pypi/v/jupyter-mcp-server)](https://pypi.org/project/jupyter-mcp-server) [![значок кузнеца](https://smithery.ai/badge/@datalayer/jupyter-mcp-server)](https://smithery.ai/server/@datalayer/jupyter-mcp-server)\n\nJupyter MCP Server — это реализация сервера [Model Context Protocol](https://modelcontextprotocol.io) (MCP), которая обеспечивает взаимодействие с 📓 блокнотами Jupyter, работающими в любой JupyterLab (также работает с вашим 💻 локальным JupyterLab).\n\n![Сервер Jupyter MCP](https://assets.datalayer.tech/jupyter-mcp/jupyter-mcp-server-claude-demo.gif)\n\n## Запустить JupyterLab\n\nУбедитесь, что у вас установлено следующее. Пакет совместной работы необходим, поскольку изменения, внесенные в блокнот, можно увидеть благодаря [Jupyter Real Time Collaboration](https://jupyterlab.readthedocs.io/en/stable/user/rtc.html) .\n\n```bash\npip install jupyterlab==4.4.1 jupyter-collaboration==4.0.2 ipykernel\npip uninstall -y pycrdt datalayer_pycrdt\npip install datalayer_pycrdt==0.12.17\n```\n\nЗатем запустите JupyterLab с помощью следующей команды.\n\n```bash\njupyter lab --port 8888 --IdentityProvider.token MY_TOKEN --ip 0.0.0.0\n```\n\nВы также можете запустить `make jupyterlab` .\n\n> \\[!ПРИМЕЧАНИЕ]\n>\n> Параметр `--ip` устанавливается равным `0.0.0.0` , чтобы разрешить серверу MCP, работающему в контейнере Docker, получать доступ к локальному JupyterLab.\n\n## Использовать с Claude Desktop\n\nClaude Desktop можно загрузить [с этой страницы](https://claude.ai/download) для macOS и Windows.\n\nДля Linux мы успешно использовали этот [НЕОФИЦИАЛЬНЫЙ скрипт сборки на основе nix](https://github.com/k3d3/claude-desktop-linux-flake)\n\n```bash\n# ⚠️ UNOFFICIAL\n# You can also run `make claude-linux`\nNIXPKGS_ALLOW_UNFREE=1 nix run github:k3d3/claude-desktop-linux-flake \\\n --impure \\\n --extra-experimental-features flakes \\\n --extra-experimental-features nix-command\n```\n\nЧтобы использовать это с Claude Desktop, добавьте следующее в ваш `claude_desktop_config.json` (подробнее на [сайте документации MCP](https://modelcontextprotocol.io/quickstart/user#2-add-the-filesystem-mcp-server) ).\n\n> \\[!ВАЖНЫЙ]\n>\n> Убедитесь, что порт `SERVER_URL` и `TOKEN` совпадают с портами, используемыми в команде `jupyter lab` .\n>\n> `NOTEBOOK_PATH` должен быть указан относительно каталога, в котором был запущен JupyterLab.\n\n### Конфигурация Клода на macOS и Windows\n\n```json\n{\n \"mcpServers\": {\n \"jupyter\": {\n \"command\": \"docker\",\n \"args\": [\n \"run\",\n \"-i\",\n \"--rm\",\n \"-e\",\n \"SERVER_URL\",\n \"-e\",\n \"TOKEN\",\n \"-e\",\n \"NOTEBOOK_PATH\",\n \"datalayer/jupyter-mcp-server:latest\"\n ],\n \"env\": {\n \"SERVER_URL\": \"http://host.docker.internal:8888\",\n \"TOKEN\": \"MY_TOKEN\",\n \"NOTEBOOK_PATH\": \"notebook.ipynb\"\n }\n }\n }\n}\n```\n\n### Конфигурация Клода на Linux\n\n```bash\nCLAUDE_CONFIG=${HOME}/.config/Claude/claude_desktop_config.json\ncat < $CLAUDE_CONFIG\n{\n \"mcpServers\": {\n \"jupyter\": {\n \"command\": \"docker\",\n \"args\": [\n \"run\",\n \"-i\",\n \"--rm\",\n \"-e\",\n \"SERVER_URL\",\n \"-e\",\n \"TOKEN\",\n \"-e\",\n \"NOTEBOOK_PATH\",\n \"--network=host\",\n \"datalayer/jupyter-mcp-server:latest\"\n ],\n \"env\": {\n \"SERVER_URL\": \"http://localhost:8888\",\n \"TOKEN\": \"MY_TOKEN\",\n \"NOTEBOOK_PATH\": \"notebook.ipynb\"\n }\n }\n }\n}\nEOF\ncat $CLAUDE_CONFIG\n```\n\n## Компоненты\n\n### Инструменты\n\nВ настоящее время сервер предлагает 2 инструмента:\n\n1. `add_execute_code_cell`\n\n* Добавьте и выполните ячейку кода в блокноте Jupyter.\n* Вход:\n * `cell_content` (строка): Код для выполнения.\n* Возвращает: Вывод ячейки.\n\n2. `add_markdown_cell`\n\n* Добавьте ячейку Markdown в блокнот Jupyter.\n* Вход:\n * `cell_content` (строка): Содержимое Markdown.\n* Возвращает: сообщение об успешном выполнении.\n\n## Здание\n\nВы можете собрать образ Docker из исходного кода.\n\n```bash\nmake build-docker\n```\n\n## Установка через Smithery\n\nЧтобы автоматически установить Jupyter MCP Server для Claude Desktop через [Smithery](https://smithery.ai/server/@datalayer/jupyter-mcp-server) :\n\n```bash\nnpx -y @smithery/cli install @datalayer/jupyter-mcp-server --client claude\n```","Russian","ru-RU",{"_27":73,"_31":77,"_36":78},{"_29":74,"_31":75,"_33":76},"通过模型上下文协议实现与 Jupyter 笔记本的交互，支持在 JupyterLab 环境中执行代码和插入 markdown。","Jupyter MCP 服务器","[![数据层](https://assets.datalayer.tech/datalayer-25.svg)](https://datalayer.io)\n\n[![成为赞助商](https://img.shields.io/static/v1?label=Become%20a%20Sponsor\\&message=%E2%9D%A4\\&logo=GitHub\\&style=flat\\&color=1ABC9C)](https://github.com/sponsors/datalayer)\n\n# 🪐 ✨ Jupyter MCP 服务器\n\n[![Github Actions 状态](https://github.com/datalayer/jupyter-mcp-server/workflows/Build/badge.svg)](https://github.com/datalayer/jupyter-mcp-server/actions/workflows/build.yml) [![PyPI - 版本](https://img.shields.io/pypi/v/jupyter-mcp-server)](https://pypi.org/project/jupyter-mcp-server) [![铁匠徽章](https://smithery.ai/badge/@datalayer/jupyter-mcp-server)](https://smithery.ai/server/@datalayer/jupyter-mcp-server)\n\nJupyter MCP 服务器是一个[模型上下文协议](https://modelcontextprotocol.io)(MCP) 服务器实现，可与在任何 JupyterLab 中运行的 📓 Jupyter 笔记本进行交互（也适用于您的 💻 本地 JupyterLab）。\n\n![Jupyter MCP 服务器](https://assets.datalayer.tech/jupyter-mcp/jupyter-mcp-server-claude-demo.gif)\n\n## 启动 JupyterLab\n\n确保已安装以下软件。协作包必不可少，因为借助[Jupyter 实时协作功能](https://jupyterlab.readthedocs.io/en/stable/user/rtc.html)，您可以看到笔记本上所做的修改。\n\n```bash\npip install jupyterlab==4.4.1 jupyter-collaboration==4.0.2 ipykernel\npip uninstall -y pycrdt datalayer_pycrdt\npip install datalayer_pycrdt==0.12.17\n```\n\n然后，使用以下命令启动 JupyterLab。\n\n```bash\njupyter lab --port 8888 --IdentityProvider.token MY_TOKEN --ip 0.0.0.0\n```\n\n您也可以运行`make jupyterlab` 。\n\n> \\[！笔记]\n>\n> `--ip`设置为`0.0.0.0`以允许在 Docker 容器中运行的 MCP 服务器访问您的本地 JupyterLab。\n\n## 与 Claude Desktop 一起使用\n\n您可以[从此页面](https://claude.ai/download)下载适用于 macOS 和 Windows 的 Claude Desktop。\n\n对于 Linux，我们成功使用了[基于 nix 的非官方构建脚本](https://github.com/k3d3/claude-desktop-linux-flake)\n\n```bash\n# ⚠️ UNOFFICIAL\n# You can also run `make claude-linux`\nNIXPKGS_ALLOW_UNFREE=1 nix run github:k3d3/claude-desktop-linux-flake \\\n --impure \\\n --extra-experimental-features flakes \\\n --extra-experimental-features nix-command\n```\n\n要将其与 Claude Desktop 一起使用，请将以下内容添加到您的`claude_desktop_config.json` （在[MCP 文档网站](https://modelcontextprotocol.io/quickstart/user#2-add-the-filesystem-mcp-server)上阅读更多内容）。\n\n> \\[！重要的]\n>\n> 确保`SERVER_URL`和`TOKEN`的端口与`jupyter lab`命令中使用的端口相匹配。\n>\n> `NOTEBOOK_PATH`应该相对于启动 JupyterLab 的目录。\n\n### macOS 和 Windows 上的 Claude 配置\n\n```json\n{\n \"mcpServers\": {\n \"jupyter\": {\n \"command\": \"docker\",\n \"args\": [\n \"run\",\n \"-i\",\n \"--rm\",\n \"-e\",\n \"SERVER_URL\",\n \"-e\",\n \"TOKEN\",\n \"-e\",\n \"NOTEBOOK_PATH\",\n \"datalayer/jupyter-mcp-server:latest\"\n ],\n \"env\": {\n \"SERVER_URL\": \"http://host.docker.internal:8888\",\n \"TOKEN\": \"MY_TOKEN\",\n \"NOTEBOOK_PATH\": \"notebook.ipynb\"\n }\n }\n }\n}\n```\n\n### Linux 上的 Claude 配置\n\n```bash\nCLAUDE_CONFIG=${HOME}/.config/Claude/claude_desktop_config.json\ncat < $CLAUDE_CONFIG\n{\n \"mcpServers\": {\n \"jupyter\": {\n \"command\": \"docker\",\n \"args\": [\n \"run\",\n \"-i\",\n \"--rm\",\n \"-e\",\n \"SERVER_URL\",\n \"-e\",\n \"TOKEN\",\n \"-e\",\n \"NOTEBOOK_PATH\",\n \"--network=host\",\n \"datalayer/jupyter-mcp-server:latest\"\n ],\n \"env\": {\n \"SERVER_URL\": \"http://localhost:8888\",\n \"TOKEN\": \"MY_TOKEN\",\n \"NOTEBOOK_PATH\": \"notebook.ipynb\"\n }\n }\n }\n}\nEOF\ncat $CLAUDE_CONFIG\n```\n\n## 成分\n\n### 工具\n\n该服务器目前提供 2 种工具：\n\n1. `add_execute_code_cell`\n\n* 在 Jupyter 笔记本中添加并执行代码单元。\n* 输入：\n * `cell_content` （字符串）：要执行的代码。\n* 返回：单元格输出。\n\n2. `add_markdown_cell`\n\n* 在 Jupyter 笔记本中添加一个 markdown 单元。\n* 输入：\n * `cell_content` （字符串）：Markdown 内容。\n* 返回：成功消息。\n\n## 建筑\n\n您可以从源代码构建 Docker 镜像。\n\n```bash\nmake build-docker\n```\n\n## 通过 Smithery 安装\n\n要通过[Smithery](https://smithery.ai/server/@datalayer/jupyter-mcp-server)自动为 Claude Desktop 安装 Jupyter MCP 服务器：\n\n```bash\nnpx -y @smithery/cli install @datalayer/jupyter-mcp-server --client 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notebook file, relative to the directory where JupyterLab was started.","required",[136,131,141],"object","githubProject",{"_33":150,"_151":152},"\n\n[![Datalayer](https://assets.datalayer.tech/datalayer-25.svg)](https://datalayer.io)\n\n[![Become a Sponsor](https://img.shields.io/static/v1?label=Become%20a%20Sponsor&message=%E2%9D%A4&logo=GitHub&style=flat&color=1ABC9C)](https://github.com/sponsors/datalayer)\n\n# 🪐✨ Jupyter MCP Server\n\n[![PyPI - Version](https://img.shields.io/pypi/v/jupyter-mcp-server)](https://pypi.org/project/jupyter-mcp-server)\n\n $\"MseeP.ai$ \n\n\n[![Trust Score](https://archestra.ai/mcp-catalog/api/badge/quality/datalayer/jupyter-mcp-server)](https://archestra.ai/mcp-catalog/datalayer__jupyter-mcp-server)\n\n> 🚨 **BREAKING CHANGE** For version `0.11.0+`, `room` has been renamed to `document`. [Read more in the release notes.](https://jupyter-mcp-server.datalayer.tech/releases)\n\n**Jupyter MCP Server** is a [Model Context Protocol](https://modelcontextprotocol.io) (MCP) server implementation that enables **real-time** interaction with 📓 Jupyter Notebooks, allowing AI to edit, document and execute code for data analysis, visualization etc.\n\nCompatible with any Jupyter deployment (local, JupyterHub, ...) and with [Datalayer](https://datalayer.ai/) hosted Notebooks.\n\n## 🚀 Key Features\n\n- ⚡ **Real-time control:** Instantly view notebook changes as they happen.\n- 🔁 **Smart execution:** Automatically adjusts when a cell run fails thanks to cell output feedback.\n- 🤝 **MCP-Compatible:** Works with any MCP client, such as Claude Desktop, Cursor, Windsurf, and more.\n\n![Jupyter MCP Server Demo](https://assets.datalayer.tech/jupyter-mcp/jupyter-mcp-server-claude-demo.gif)\n\n🛠️ This MCP offers multiple tools such as `insert_execute_code_cell`, `append_markdown_cell`, `get_notebook_info`, `read_cell`, and more, enabling advanced interactions with Jupyter notebooks. Explore our [tools documentation](https://jupyter-mcp-server.datalayer.tech/tools) to learn about all the tools powering Jupyter MCP Server.\n\n## 🏁 Getting Started\n\nFor comprehensive setup instructions—including `Streamable HTTP` transport and advanced configuration—check out [our documentation](https://jupyter-mcp-server.datalayer.tech/). Or, get started quickly with `JupyterLab` and `stdio` transport here below.\n\n### 1. Set Up Your Environment\n\n```bash\npip install jupyterlab==4.4.1 jupyter-collaboration==4.0.2 ipykernel\npip uninstall -y pycrdt datalayer_pycrdt\npip install datalayer_pycrdt==0.12.17\n```\n\n### 2. Start JupyterLab\n\n```bash\n# make jupyterlab\njupyter lab --port 8888 --IdentityProvider.token MY_TOKEN --ip 0.0.0.0\n```\n\n### 3. Configure Your Preferred MCP Client\n\n> [!NOTE]\n>\n> Ensure the `port` of the `DOCUMENT_URL` and `RUNTIME_URL` match those used in the `jupyter lab` command.\n>\n> The `DOCUMENT_ID` which is the path to the notebook you want to connect to, should be relative to the directory where JupyterLab was started.\n>\n> In a basic setup, `DOCUMENT_URL` and `RUNTIME_URL` are the same. `DOCUMENT_TOKEN`, and `RUNTIME_TOKEN` are also the same and is actually the Jupyter Token.\n\n#### MacOS and Windows\n\n```json\n{\n \"mcpServers\": {\n \"jupyter\": {\n \"command\": \"docker\",\n \"args\": [\n \"run\",\n \"-i\",\n \"--rm\",\n \"-e\",\n \"DOCUMENT_URL\",\n \"-e\",\n \"DOCUMENT_TOKEN\",\n \"-e\",\n \"DOCUMENT_ID\",\n \"-e\",\n \"RUNTIME_URL\",\n \"-e\",\n \"RUNTIME_TOKEN\",\n \"datalayer/jupyter-mcp-server:latest\"\n ],\n \"env\": {\n \"DOCUMENT_URL\": \"http://host.docker.internal:8888\",\n \"DOCUMENT_TOKEN\": \"MY_TOKEN\",\n \"DOCUMENT_ID\": \"notebook.ipynb\",\n \"RUNTIME_URL\": \"http://host.docker.internal:8888\",\n \"RUNTIME_TOKEN\": \"MY_TOKEN\"\n }\n }\n }\n}\n```\n\n#### Linux\n\n```json\n{\n \"mcpServers\": {\n \"jupyter\": {\n \"command\": \"docker\",\n \"args\": [\n \"run\",\n \"-i\",\n \"--rm\",\n \"-e\",\n \"DOCUMENT_URL\",\n \"-e\",\n \"DOCUMENT_TOKEN\",\n \"-e\",\n \"DOCUMENT_ID\",\n \"-e\",\n \"RUNTIME_URL\",\n \"-e\",\n \"RUNTIME_TOKEN\",\n \"--network=host\",\n \"datalayer/jupyter-mcp-server:latest\"\n ],\n \"env\": {\n \"DOCUMENT_URL\": \"http://localhost:8888\",\n \"DOCUMENT_TOKEN\": \"MY_TOKEN\",\n \"DOCUMENT_ID\": \"notebook.ipynb\",\n \"RUNTIME_URL\": \"http://localhost:8888\",\n \"RUNTIME_TOKEN\": \"MY_TOKEN\"\n }\n }\n }\n}\n```\n\nFor detailed instructions on configuring various MCP clients—including [Claude Desktop](https://jupyter-mcp-server.datalayer.tech/clients/claude_desktop), [VS Code](https://jupyter-mcp-server.datalayer.tech/clients/vscode), [Cursor](https://jupyter-mcp-server.datalayer.tech/clients/cursor), [Cline](https://jupyter-mcp-server.datalayer.tech/clients/cline), and [Windsurf](https://jupyter-mcp-server.datalayer.tech/clients/windsurf) — see the [Clients documentation](https://jupyter-mcp-server.datalayer.tech/clients).\n\n## 📚 Resources\n\nLooking for blog posts, videos, or other materials about Jupyter MCP Server?\n\n👉 Visit the [Resources section](https://jupyter-mcp-server.datalayer.tech/resources).\n","url","https://github.com/datalayer/jupyter-mcp-server","githubRepository",{"_155":156,"_157":158,"_159":160,"_161":162,"_31":163,"_117":164,"_166":167,"_168":169,"_171":172},"defaultBranch","main","fullName","datalayer/jupyter-mcp-server","githubTreeId",209468,"language","Jupyter Notebook","jupyter-mcp-server",{"_121":122,"_106":165},"suvlid46mg","recentStargazers",11,"spdxLicense",{"_31":170},"BSD 3-Clause","stargazers",655,"glamaJson","inspectable",false,"npmPackage","supportedPlatforms",[179,180,181],"MACOS","WINDOWS","LINUX","scores",{"_184":185,"_186":-5,"_187":-5},"license",100,"quality","security","simpleIcon","toolCount",0,"et849kq742","updatedAt","2025-09-10T21:34:47.721686Z","meta",[196,199,201,208,211,213,216,219,222,225,228,230,233,236,238,240,242,244,246],{"_197":198},"title","Jupyter MCP Server | Glama",{"_200":40,"_31":29},"content",{"_202":203,"_204":205,"_206":207},"href","https://glama.ai/mcp/servers/@datalayer/jupyter-mcp-server","rel","canonical","tagName","link",{"_200":32,"_209":210},"property","og:title",{"_200":40,"_209":212},"og:description",{"_200":214,"_209":215},"https://glama.ai/logo.png","og:logo",{"_200":217,"_209":218},"https://glama.ai/mcp/servers/@datalayer/jupyter-mcp-server/og-image","og:image",{"_200":220,"_209":221},"630","og:image:height",{"_200":223,"_209":224},"1200","og:image:width",{"_200":226,"_209":227},"website","og:type",{"_200":203,"_209":229},"og:url",{"_200":231,"_209":232},"Glama – MCP Hosting Platform","og:site_name",{"_200":234,"_31":235},"summary_large_image","twitter:card",{"_200":231,"_31":237},"twitter:title",{"_200":40,"_31":239},"twitter:description",{"_200":217,"_31":241},"twitter:image",{"_200":220,"_31":243},"twitter:image:height",{"_200":223,"_31":245},"twitter:image:width",{"_247":248},"script:ld+json",{"_249":250,"_251":252},"@context","http://schema.org","@graph",[253],{"_249":254,"_255":256,"_257":258},"https://schema.org","@type","BreadcrumbList","itemListElement",[259,266,270,274],{"_255":260,"_261":262,"_31":263,"_264":265},"ListItem","item","/","Glama","position",1,{"_255":260,"_261":267,"_31":268,"_264":269},"/mcp","MCP",2,{"_255":260,"_261":271,"_31":272,"_264":273},"/mcp/servers","Servers",3,{"_255":260,"_261":275,"_31":32,"_264":276},"/mcp/servers/@datalayer/jupyter-mcp-server",4,"visitorIsMaintainer","routes/_public/mcp/servers/~namespace/~slug/_pages/_explorer/blob/~branch/~/_route",{"_280":281,"_285":156,"_27":286,"_194":309,"_333":334,"_335":336},"blob",{"_200":282,"_283":284},"; 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MCP directory API