jupyter-mcp-server — Claude MCP 必备工具中文教程
jupyter-mcp-server — Claude MCP 必备工具中文教程 是 AI Skill Hub 本期精选MCP工具之一。已获得 1.1k 颗 GitHub Star，综合评分 8.4 分，整体质量较高。我们强烈推荐将其纳入你的 AI 工具库，帮助提升工作效率。
通过安装 jupyter-mcp-server — Claude MCP 必备工具中文教程，你的 AI 助手将获得额外的工具调用能力，可以用自然语言直接操控该工具的功能，无需学习复杂的命令行语法。MCP 工具的核心价值在于"一次配置，永久增强"——配置完成后，每次与 AI 对话时都可以无缝调用这些工具。
在技术实现上，MCP 工具通过标准的 JSON-RPC 协议与 AI 客户端通信，工具的功能以"工具列表"的形式暴露给 AI 模型，AI 可以按需调用。jupyter-mcp-server — Claude MCP 必备工具中文教程 提供了结构化的工具调用接口，使 AI 模型能够精确地理解和使用每个功能点，显著降低 AI 在工具使用上的错误率。
与传统的 API 集成相比，MCP 工具的优势在于无需编写代码——用户只需在配置文件中添加几行 JSON，即可让 AI 获得全新能力。AI Skill Hub 将 jupyter-mcp-server — Claude MCP 必备工具中文教程 评为 AI 评分 8.4 分，属于同类工具中的优质选择。
jupyter-mcp-server — Claude MCP 必备工具中文教程 是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。
jupyter-mcp-server — Claude MCP 必备工具中文教程 是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。
- 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
- 提供结构化工具调用接口，显著降低 AI 集成复杂度
- 支持 Claude Desktop 和 Claude Code 无缝接入，开箱即用
- 可与其他 MCP 工具组合叠加，构建完整 AI 工作站
- 轻量无侵入设计，不影响现有系统架构
- 在 Claude Desktop 对话中直接调用本地工具，实现 AI 与系统的深度联动
- 通过自然语言驱动复杂的多步骤自动化任务，代替繁琐手动操作
- 将多个 MCP 工具组合使用，构建个人专属 AI 工作站
# 方式一：通过 Claude Code CLI 一键安装
claude skill install https://github.com/datalayer/jupyter-mcp-server
# 方式二：手动配置 claude_desktop_config.json
{
"mcpServers": {
"jupyter-mcp-server---claude-mcp---------": {
"command": "npx",
"args": ["-y", "jupyter-mcp-server"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
- 确认已安装 Node.js（v18 或以上版本）
- 打开 Claude Desktop 或 Claude Code 的 MCP 配置文件
- 按「交给 Agent 安装 → Claude Desktop」标签中的 JSON 配置填入 mcpServers 字段
- 保存配置文件并重启 Claude 客户端
- 重启后，在对话中即可使用本工具
# 安装后在 Claude 对话中直接使用 # 示例： 用户: 请帮我用 jupyter-mcp-server — Claude MCP 必备工具中文教程 执行以下任务... Claude: [自动调用 jupyter-mcp-server — Claude MCP 必备工具中文教程 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入："列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"jupyter-mcp-server___claude_mcp_________": {
"command": "npx",
"args": ["-y", "jupyter-mcp-server"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
简介
🔧 MCP Overview
🔧 Tools Overview
The server provides a rich set of tools for interacting with Jupyter notebooks, categorized as follows. For more details on each tool, their parameters, and return values, please refer to the official Tools documentation.
Server Management Tools
| Name | Description |
|---|---|
list_files | List files and directories in the Jupyter server's file system. |
list_kernels | List all available and running kernel sessions on the Jupyter server. |
connect_to_jupyter | Connect to a Jupyter server dynamically without restarting the MCP server. *Not available when running as Jupyter extension. Useful for switching servers dynamically or avoiding hardcoded configuration.* [Read more](https://jupyter-mcp-server.datalayer.tech/reference/tools/#3-connect_to_jupyter) |
Multi-Notebook Management Tools
| Name | Description |
|---|---|
use_notebook | Connect to a notebook file, create a new one, or switch between notebooks. |
list_notebooks | List all notebooks available on the Jupyter server and their status |
restart_notebook | Restart the kernel for a specific managed notebook. |
unuse_notebook | Disconnect from a specific notebook and release its resources. |
read_notebook | Read notebook cells source content with brief or detailed format options. |
Cell Operations and Execution Tools
| Name | Description |
|---|---|
read_cell | Read the full content (Metadata, Source and Outputs) of a single cell. |
insert_cell | Insert a new code or markdown cell at a specified position. |
delete_cell | Delete a cell at a specified index. |
move_cell | Move a cell from one position to another within a notebook. |
overwrite_cell_source | Overwrite the source code of an existing cell. |
edit_cell_source | Apply surgical find-and-replace edits to a cell's source without full rewrite. |
execute_cell | Execute a cell with timeout, supports multimodal output including images. |
insert_execute_code_cell | Insert a new code cell and execute it in one step. |
execute_code | Execute code directly in the kernel, supports magic commands and shell commands. |
JupyterLab Integration
Available only when JupyterLab mode is enabled. It is enabled by default.
When running in JupyterLab mode, Jupyter MCP Server integrates with jupyter-mcp-tools to expose additional JupyterLab commands as MCP tools. By default, the following tools are enabled:
| Name | Description |
|---|---|
notebook_run-all-cells | Execute all cells in the current notebook sequentially |
notebook_get-selected-cell | Get information about the currently selected cell |
<details> <summary><strong>📚 Learn how to customize additional tools</strong></summary>
You can now customize which tools from jupyter-mcp-tools are available using the allowed_jupyter_mcp_tools configuration parameter. This allows you to enable additional notebook operations, console commands, file management tools, and more.
```bash
📝 Prompt Overview
The server also supports prompt feature of MCP, providing a easy way for user to interact with Jupyter notebooks.
| Name | Description |
|---|---|
jupyter-cite | Cite specific cells from specified notebook (like @ in Coding IDE or CLI) |
For more details on each prompt, their input parameters, and return content, please refer to the official Prompt documentation.
🚀 Key Features
- ⚡ Real-time control: Instantly view notebook changes as they happen.
- 🔁 Smart execution: Automatically adjusts when a cell run fails thanks to cell output feedback.
- 🧠 Context-aware: Understands the entire notebook context for more relevant interactions.
- 📊 Multimodal support: Support different output types, including images, plots, and text.
- 📚 Multi-notebook support: Seamlessly switch between multiple notebooks.
- 🎨 JupyterLab integration: Enhanced UI integration like automatic notebook opening.
- 🤝 MCP-compatible: Works with any MCP client, such as Claude Desktop, Cursor, Windsurf, and more.
- 🔍 Observability: Built-in hook system with OpenTelemetry integration for tracing tool calls and kernel executions.
Compatible with any Jupyter deployment (local, JupyterHub, ...) and with Datalayer hosted Notebooks.
🏁 Getting Started
For comprehensive setup instructions—including Streamable HTTP transport, running as a Jupyter Server extension and advanced configuration—check out our documentation. Or, get started quickly with JupyterLab and STDIO transport here below.
Hosted deployment
A hosted deployment is available on Fronteir AI.
Example: Enable additional tools via command-line
jupyter lab --port 4040 --IdentityProvider.token MY_TOKEN --JupyterMCPServerExtensionApp.allowed_jupyter_mcp_tools="notebook_run-all-cells,notebook_get-selected-cell,notebook_append-execute,console_create" ```
For the complete list of available tools and detailed configuration instructions, please refer to the Additional Tools documentation.
</details>
1. Set Up Your Environment
pip install jupyterlab==4.4.1 jupyter-collaboration==4.0.2 jupyter-mcp-tools>=0.1.4 ipykernel pycrdt[!TIP] To confirm your environment is correctly configured: 1. Open a notebook in JupyterLab 2. Type some content in any cell (code or markdown) 3. Observe the tab indicator: you should see an "×" appear next to the notebook name, indicating unsaved changes 4. Wait a few seconds—the "×" should automatically change to a "●" without manually saving This automatic saving behavior confirms that the real-time collaboration features are working properly, which is essential for MCP server integration.
Start JupyterLab on port 8888, allowing access from any IP and setting a token
jupyter lab --port 8888 --IdentityProvider.token MY_TOKEN --ip 0.0.0.0 ```
[!NOTE] If you are running notebooks through JupyterHub instead of JupyterLab as above, refer to our JupyterHub setup guide.
3. Configure Your Preferred MCP Client
Next, configure your MCP client to connect to the server. We offer two primary methods—choose the one that best fits your needs:
- 📦 Using
uvx(Recommended for Quick Start): A lightweight and fast method usinguv. Ideal for local development and first-time users. - 🐳 Using
Docker(Recommended for Production): A containerized approach that ensures a consistent and isolated environment, perfect for production or complex setups.
<details> <summary><b>📦 Using uvx (Quick Start)</b></summary>
First, install uv:
```bash pip install uv uv --version
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- 生产部署优先使用 Docker Compose 隔离依赖，并挂载 volume 持久化数据
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- 容器内无法访问宿主机 localhost — 使用 host.docker.internal
- Python 依赖冲突：建议用 venv / uv 隔离环境
- Docker：jupyter-mcp-server 提供官方镜像，docker compose up 一键启动
- CLI：直接 npm install -g / pip install，命令行调用
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
- 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
- 提供结构化工具调用接口，显著降低 AI 集成复杂度
- 支持 Claude Desktop 和 Claude Code 无缝接入，开箱即用
- 可与其他 MCP 工具组合叠加，构建完整 AI 工作站
- 轻量无侵入设计，不影响现有系统架构
- 在 Claude Desktop 对话中直接调用本地工具，实现 AI 与系统的深度联动
- 通过自然语言驱动复杂的多步骤自动化任务，代替繁琐手动操作
- 将多个 MCP 工具组合使用，构建个人专属 AI 工作站
- +BSD-3-Clause 协议，可免费商用
- +标准化 MCP 协议，生态互联性强
- +与 Claude 官方生态无缝对接
- +即插即用，配置简单快捷
- −依赖 Claude 客户端，非 Claude 用户无法使用
- −MCP 协议仍在持续演进，接口可能变更
- −需要一定的配置步骤
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ BSD 3-Clause — 宽松协议，可商用修改分发，禁止使用原作者名称进行背书宣传。
经综合评估，jupyter-mcp-server — Claude MCP 必备工具中文教程 在MCP工具赛道中表现稳健，质量优秀。如果你已有明确的使用需求，可以直接上手体验；如果还在评估阶段，建议对比同类工具后再做决策。
| 原始名称 | jupyter-mcp-server |
| 原始描述 | 🪐 🔧 Model Context Protocol (MCP) Server for Jupyter. |
| Topics | aijupytermcpmcp-servertools |
| GitHub | https://github.com/datalayer/jupyter-mcp-server |
| License | BSD-3-Clause |
| 语言 | Python |
收录时间：2026-05-22 · 更新时间：2026-05-22 · License：BSD-3-Clause · AI Skill Hub 不对第三方内容的准确性作法律背书。
🤖 交给 Agent 安装 · jupyter-mcp-server — Claude MCP 必备工具中文教程
选择 Agent 类型，复制安装指令后粘贴到对应客户端