Jupyter MCP server
io.github.ChengJiale150/jupyter-mcp-server·v1.1.0·9 starsA powerful MCP server for AI-driven Jupyter Notebook management and execution
9 stars84 downloads/wk
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Jupyter tools (12, 5 write)
write = sends, deletes, buys or postsRead from the package source without running it. The installed server may list more.
append_execute_cellwrite actionAdd a new cell to the end of a Notebook and immediately execute it. It will return the output of the cell. It is highly recommended for replacing the combination of `insert_cell` and `execute_cell` at the end of the Notebook.
connect_notebookConnect to a Notebook and corresponding Kernel. It is the FIRST STEP before using following tools.
delete_cellwrite actionDelete a specific cell. When deleting many cells, MUST delete them in descending order of their index.
execute_cellwrite actionExecute a specific cell with a timeout. It will return the output of the cell.
execute_temporary_cellwrite actionExecute a temporary code block (not saved to the Notebook) and will return the output.
insert_cellwrite actionInsert a cell above/below of a anchor index. When inserting many cells, MUST insert them in ascending order of their index.
list_cellList the basic information of all cells. It will return Index, Type, Execution Count and First Line of the Cell. It will be used to quickly overview the structure and current status of the Notebook or locate the index of specific cells for following operations(e.g. delete, insert).
list_notebookList all currently connected Notebooks. It will return unique name, Jupyter URL and Path of all connected Notebooks
overwrite_cellOverwrite the content of a specific cell
read_cellRead the detailed content of a specific cell. It will return the source code, execution count and output of the cell.
read_notebookRead the source content (without output) of a connected Notebook. It will return the formatted content of the Notebook (including Index, Cell Type, Execution Count and Full Source Content). ONLY used when the user explicitly instructs to read the full content of the Notebook.
restart_notebookRestart the kernel of a specified Notebook, clear all imported packages and variables
Public scan report
scanner v0.1.10 · 2026-10-01 · same rubric, same numbers if you re-run it
- Code scan8 source files scanned25/25
- –Live reliabilityno gateway calls yet and no remote to proben/a
- –Tool poisoningtools not inspected (local package is not executed); not countedn/a
- Auth qualitylocal package, no credentials required12/15
- Maintenancelast push 335 days ago4/15
- Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
How Jupyter compares
Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.
- Safety score
- 75/100
- higher than 45% of 48,255 servers
- Tools
- 12
- the typical server has 7
- Tools that change things
- 42%
- typical: 0%. Fewer write tools means less an agent can break
- GitHub stars
- 9
- more than 87% of 28,688 servers
- Downloads a week
- 84
- more than 59% of 12,191 servers with a package
What its tools let an agent do: 6 read · 1 create · 1 change · 1 delete · 3 run or deploy
Install Jupyter in Claude Code, Cursor or VS Code
claude mcp add jupyter-mcp-server -- uvx better-jupyter-mcp-server
What the publisher says
From the Jupyter repository's README, as published. We do not edit it. Read it on GitHub
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🪐 Jupyter MCP Server
专门为AI连接与管理Jupyter Notebook而开发的MCP服务
由 ChengJiale150 开发
English | 中文
📖 目录
- 项目简介
- 工具一览
- 快速上手
- 最佳实践
- 贡献指南
- 致谢
🎯 项目简介
Jupyter MCP Server 是一个基于 Model Context Protocol (MCP) 的服务,为目前最先进的的AI IDE(如 Cursor) 与CLI工具(如Gemini CLI)提供连接与管理Jupyter Notebook的能力。使得AI能够操作Notebook,进行数据分析、可视化、机器学习等任务。
🤔 为什么需要Jupyter MCP Server
Jupyter Notebook 是数据科学家最常用的工具之一,它提供了一个交互式的环境,使其可以方便地进行数据分析、可视化、机器学习等探索性任务。然而,由于Notebook自身的格式限制,使得其难以像纯文本文件(如Markdown、Python文件)一样被AI直接理解。
现有的提供操作Notebook的工具或MCP服务,要么仅能阅读与编辑Notebook,要么仅能操纵单个Notebook,难以满足同时操纵多个Notebook的复杂需求。此外,大多数工具也不支持多模态输出,无法充分利用目前最先进的多模态大模型(如Gemini 2.5)的强大图文理解能力。
Jupyter MCP Server 就是为了解决这个问题而开发的。它通过MCP协议,向AI提供了管理Jupyter Kernel与Notebook的工具,使其能够操纵多个Notebook进行交互式的任务执行,并输出多模态结果,助力数据科学家提高分析效率。
✨ 关键亮点
- 🔌 MCP兼容: 能够在任何支持MCP协议的IDE或CLI工具中使用
- 📚 多Notebook管理: 支持同时管理多个Notebook
- 🔁 交互式执行: 能够根据Cell的输出自动调整执行策略
- 📊 多模态输出: 支持输出多模态结果,如文本、图片、表格等
🔧 工具一览
Notebook管理模块
Cell基本功能模块
Cell高级集成功能模块
工具的具体内容详见工具文档
🛠️ 快速上手
环境准备
- Python 3.12+(推荐使用Anaconda)
- uv(安装详见安装指南)
安装Jupyter MCP Server
uvx 快速安装(推荐)
在安装uv后,直接配置MCP的JSON格式即可,示例如下:
Shortened. The full README is on GitHub.
Nothing above is checked by us. What we check is on the safety report.
Jupyter: common questions
- Is Jupyter MCP server safe?
- Mostly: it is graded B (75/100). Read the Jupyter safety report
- How do I install Jupyter?
- It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
- Does Jupyter need an API key?
- Not as far as the registry entry and our scan can tell: no credentials are declared or required.
- Is Jupyter maintained?
- The last commit was 368 days ago (2025-11-01). The latest release is v1.1.0.
- How does Jupyter compare with other MCP servers?
- safety score 75/100 (higher than 45% of 48,255 servers); tools 12 (the typical server has 7); tools that change things 42% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.