Jupyter Kernel MCP

Notebook Agent — Stateful Notebook Execution System

A local notebook execution system that lets AI agents run Jupyter notebook cells with persistent kernel state, output persistence, and structured JSON control surface.

Quick Start

Install

# GitHub에서 직접 설치
pip install git+https://github.com/KJH-Sun/jupyter-kernel-mcp.git

# 또는 로컬 클론 후 설치
git clone https://github.com/KJH-Sun/jupyter-kernel-mcp.git
cd notebook-agent
pip install -e ".[dev]"

Update

pip install --no-cache-dir --force-reinstall git+https://github.com/KJH-Sun/jupyter-kernel-mcp.git

버전 번호가 동일하면 pip이 캐시를 재사용하므로 --no-cache-dir --force-reinstall 플래그가 필요합니다. 설치 후 Claude Code에서 MCP 서버를 재시작해야 변경사항이 반영됩니다.

Claude Code MCP 서버로 사용

설치 후 프로젝트의 .mcp.json에 추가:

{
  "mcpServers": {
    "notebook-runtime": {
      "command": "notebook-agent-mcp",
      "args": []
    }
  }
}

Claude Code를 (재)시작하면 다음 도구들이 자동으로 사용 가능해집니다:

• open_notebook — 노트북 열기 + 커널 시작
• list_cells — 셀 목록 조회
• run_cell — 단일 셀 실행
• run_until — 처음부터 N번 셀까지 실행
• restart_kernel — 커널 재시작
• list_sessions — 활성 세션 조회
• get_cell_output — 셀 출력 조회 + 이미지 추출
• shutdown_idle — 유휴 커널 종료
• save_notebook — 노트북 저장

Run the FastAPI Server

uvicorn app.main:app --host 127.0.0.1 --port 8000

Use the CLI (no server required)

# Open a notebook (starts kernel)
notebook-agent open --path /path/to/notebook.ipynb

# List cells
notebook-agent list-cells --path /path/to/notebook.ipynb

# Run a single cell (0-based index)
notebook-agent run-cell --path /path/to/notebook.ipynb --cell 0

# Run all cells up to index 5 with fresh kernel
notebook-agent run-until --path /path/to/notebook.ipynb --cell 5 --mode restart_and_run_until

# Restart kernel
notebook-agent restart-kernel --path /path/to/notebook.ipynb

# List active sessions
notebook-agent sessions

# Shutdown idle kernels
notebook-agent shutdown-idle --max-idle 1800

# Read cell outputs and extract images
notebook-agent get-cell-output --path /path/to/notebook.ipynb --cell 3

# Save notebook
notebook-agent save --path /path/to/notebook.ipynb

All CLI commands output structured JSON.

Execution Modes

reuse_existing_session (default)

Reuses the existing kernel session. Variables, imports, and state from prior cell executions are preserved. Fast — only runs the requested cell.

Use when: running cells sequentially in order, or when prior cells have already been executed.

restart_and_run_until

Shuts down the current kernel, starts a fresh one, then runs all code cells from cell 0 through the target cell. Guarantees clean, reproducible state.

Use when:

• A cell fails with NameError or ImportError (missing prior state)
• You want to ensure reproducible results
• The user asks to "run from scratch"

Example Agent Workflow

# 1. Open notebook
notebook-agent open --path analysis.ipynb

# 2. Check cells
notebook-agent list-cells --path analysis.ipynb

# 3. Run cells in order
notebook-agent run-cell --path analysis.ipynb --cell 0
notebook-agent run-cell --path analysis.ipynb --cell 1

# 4. If cell 3 fails with NameError, retry with full state rebuild
notebook-agent run-cell --path analysis.ipynb --cell 3 --mode restart_and_run_until

# 5. Check image outputs from a cell (e.g. matplotlib chart)
notebook-agent get-cell-output --path analysis.ipynb --cell 2
# → image_paths: ["/tmp/notebook-agent/analysis/cell_2_0.png"]

HTTP API

When the FastAPI server is running:

Architecture

See docs/architecture.md for detailed component design.

Agent Usage Guide

See docs/agent_skill.md for instructions on how an AI agent should use this system.

Tests

pytest

Tests use real Jupyter kernels — requires ipykernel installed.