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Jupyter MCP Server

@datalayer

代码仓库主页

基本信息

分类

数据与分析

许可证

BSD-3-Clause

传输方式

stdio

作者

datalayer

关于 Jupyter MCP Server

🪐 🔧 Model Context Protocol (MCP) Server for Jupyter.

#tools#ai#jupyter#mcp#mcp-server

Jupyter MCP Server 配置

暂无标准配置

该服务器的 README 中没有可解析的 MCP 配置块，请前往代码仓库查看安装说明。

代码仓库

Jupyter MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is 🪐✨ Jupyter MCP Server?

An open-source MCP server that enables AI agents to connect to and manage Jupyter Notebooks in real-time. It bridges AI assistants with Jupyter environments, allowing direct notebook creation, cell execution, and output retrieval. Developed by Datalayer.

How to use 🪐✨ Jupyter MCP Server?

Install via pip install jupyter-mcp-server and configure your MCP client with environment variables JUPYTER_URL, JUPYTER_TOKEN, and ALLOW_IMG_OUTPUT. Run using uvx for quick start or Docker for production. Alternatively, run as a Jupyter Server extension. The server supports STDIO and Streamable HTTP transports.

Key features of 🪐✨ Jupyter MCP Server

  • Real-time notebook control with instant output viewing.
  • Smart execution that adjusts after cell failures.
  • Context-aware understanding of entire notebooks.
  • Multimodal output support (images, plots, text).
  • Multi-notebook switching and management.
  • JupyterLab integration with additional commands.

Use cases of 🪐✨ Jupyter MCP Server

  • AI-assisted code development and debugging in Jupyter.
  • Automated data analysis and visualization workflows.
  • Interactive exploration of notebooks via natural language.
  • Orchestrating multi-notebook experiments with an AI agent.
  • Real-time collaborative editing with JupyterLab integration.

常见问题

1

What MCP clients are supported?

Works with any MCP client, including Claude Desktop, Cursor, Windsurf, and others.

2

How do I configure the server?

Set `JUPYTER_URL`, `JUPYTER_TOKEN`, and `ALLOW_IMG_OUTPUT` environment variables. A `MCP_TOKEN` is also required since version 1.0.0.

3

Is Docker supported?

Yes. Use the `datalayer/jupyter-mcp-server` Docker image with environment variables passed at runtime.

4

What are the runtime requirements?

Python environment with JupyterLab 4.4.1+, `jupyter-collaboration`, `jupyter-mcp-tools`, `ipykernel`, and `pycrdt`. Starting v1.0.2, `datalayer_pycrdt` is no longer needed.

5

Does it support JupyterHub or Google Colab?

Support for JupyterHub and Google Colab deployments is actively being developed; user feedback is welcome.

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