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KameniAlexNea
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jupyter-mcp

Automates JupyterLab sessions for executing Python code and managing notebooks.

Updated Feb 24, 2026
Search Tags:#automation#development#mcp#code execution#jupyter#notebook
One-click install
npx skills add https://github.com/KameniAlexNea/gladius-agent --skill jupyter-mcp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: jupyter-mcp
Source: https://github.com/KameniAlexNea/gladius-agent/tree/main/gladius/utils/templates/skills/jupyter-mcp
Command: npx skills add https://github.com/KameniAlexNea/gladius-agent --skill jupyter-mcp

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jupyter-mcp-server, jupyterlab, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill allows you to programmatically interact with Jupyter notebooks, enabling automated cell execution, code writing, and output retrieval, bridging the gap between AI agents and interactive development environments.

Core Features & Use Cases

  • Jupyter Lab Server Management: Starts and manages a Jupyter Lab server accessible via MCP.
  • Notebook Interaction: Provides tools to read, append, execute, and replace notebook cells.
  • Use Case: An AI agent can use this Skill to automatically generate Python code within a notebook, execute it to train a model, and then read the resulting metrics, all without direct human intervention.

Quick Start

Start a Jupyter Lab server and prepare for notebook interaction.

Frequently Asked Questions about jupyter-mcp

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate Jupyter notebook cell execution through an AI agent?▼

You can automate Jupyter notebook cell execution by using this Skill to initiate a Jupyter Lab server and configure the jupyter-mcp-server, enabling AI agents to read, write, and execute notebook cells programmatically.

What is programmatic notebook manipulation via MCP?▼

Programmatic notebook manipulation via MCP allows AI agents to interact with Jupyter notebooks by appending, replacing, reading, and executing code cells using mcp__jupyter__* tools without direct human intervention.

Do I need a Jupyter Lab server running to use MCP notebook interaction?▼

Yes, you need a Jupyter Lab server running on localhost:8888 and the jupyter-mcp-server installed to enable AI agents to interact with your notebooks through MCP tools.

Can an AI agent automatically write Python code and retrieve metrics from a Jupyter notebook?▼

Yes, an AI agent can automatically generate Python code within a Jupyter notebook, execute it to train a model, and read the resulting metrics by leveraging the configured jupyter-mcp-server.

What are the limitations of using jupyter-mcp-server for code execution?▼

The jupyter-mcp-server requires jupyterlab to be installed and the server to be running on localhost:8888, meaning your code execution and notebook interaction are constrained to this local environment setup.

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