overthinkos/jupyter

jupyter

Lightweight JupyterLab with real-time collaboration via jupyter-collaboration (Y-CRDT).

Image Properties

| Property | Value | |----------|-------| | Base | fedora | | Layers | agent-forwarding, jupyter (sub-layers: jupyter-mcp), notebook-templates, dbus, ov | | Platforms | linux/amd64, linux/arm64 | | Ports | 8888 | | Registry | ghcr.io/overthinkos |

Full Layer Stack

1. fedora (Fedora 43 base — no GPU)
2. pixi → python → supervisord (transitive)
3. jupyter — JupyterLab + jupyter-collaboration + data science (composes jupyter-mcp sub-layer for MCP extension)
4. notebook-templates — Starter notebooks (data layer, seeds ~/workspace)
5. agent-forwarding — SSH/GPG agent forwarding
6. dbus — D-Bus session bus
7. ov — ov CLI binary

Ports

| Port | Service | Protocol | |------|---------|----------| | 8888 | JupyterLab | HTTP |

Volumes

| Name | Path | Purpose | |------|------|---------| | workspace | ~/workspace | Persistent notebook storage |

Quick Start

ov image build jupyter
ov config jupyter
ov start jupyter
# Open http://localhost:8888

Key Layers

• /ov-layers:jupyter — JupyterLab + collaboration + data science
• /ov-layers:agent-forwarding — SSH/GPG forwarding

Related Images

• /ov-images:jupyter-ml — GPU-accelerated variant (nvidia base, full ML stack)
• /ov-images:jupyter-ml-notebook — GPU variant with fine-tuning notebooks
• /ov-images:openwebui — Open WebUI consumes jupyter for code execution and MCP tools
• /ov-images:hermes — Hermes agent consumes jupyter MCP for notebook manipulation
• /ov-images:fedora — parent base image

MCP Server (Programmatic Notebook Access)

The image includes a built-in MCP server at http://localhost:8888/mcp (Streamable HTTP transport, MCP spec 2025-11-25). AI agents can create, read, edit, execute, and watch notebooks programmatically — with changes syncing live to all collaborators via CRDT.

The MCP server name is set via the MCP_SERVER_NAME environment variable (default: jupyter). For multi-instance deployments, override per-instance: ov config jupyter -i work -e MCP_SERVER_NAME=jupyter-work.

Configure Claude Code

The ov-jupyter plugin in plugins/ov-jupyter/ declares the MCP server. To connect manually:

claude mcp add --transport http --scope project jupyter http://localhost:8888/mcp

This creates .mcp.json at the project root:

{"mcpServers": {"jupyter": {"type": "http", "url": "http://localhost:8888/mcp"}}}

13 MCP tools

| Category | Tools | |----------|-------| | Notebook management | list_notebooks, get_notebook, create_notebook | | Cell operations (CRDT) | get_cell, update_cell, insert_cell, delete_cell, execute_cell | | Room management | open_notebook_session, close_notebook_session | | Change watching | watch_notebook (blocks until change or timeout) | | Collaboration | get_active_users, get_active_sessions |

See /ov-layers:jupyter for full parameter and return type documentation.

Testing with claude -p

# Prerequisites: ov start jupyter

# Create and work with a notebook
claude -p "Call create_notebook with path 'test.ipynb'"
claude -p "Call open_notebook_session with path 'test.ipynb'"
claude -p "Call insert_cell with path 'test.ipynb', index 0, source 'print(42)', cell_type 'code'"
claude -p "Call execute_cell with path 'test.ipynb' and index 0"
claude -p "Call close_notebook_session with path 'test.ipynb'"

Multi-client collaboration

Multiple claude -p sessions (or any MCP client) can edit the same notebook simultaneously. Each session creates a separate HTTP connection but shares the same CRDT document. Changes from one client are immediately visible to all others.

# Client A watches for changes
claude -p "Call watch_notebook with path 'test.ipynb' and timeout 30" &

# Client B makes a change
claude -p "Call update_cell with path 'test.ipynb', index 0, source 'print(\"hello\")'"

# Client A's watch_notebook returns: {"changed": true, "cell_count": 1}

Verification

After ov start:

# Container and services running
ov status jupyter
ov service status jupyter

# JupyterLab responds
curl -s -o /dev/null -w '%{http_code}' http://localhost:8888    # 200

# MCP server responds
curl -s http://localhost:8888/mcp -X POST \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json, text/event-stream' \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-11-25","capabilities":{},"clientInfo":{"name":"test","version":"0.1.0"}}}'
# Expected: SSE response with serverInfo.name = "jupyter"

# Server extensions
ov shell jupyter -c "jupyter server extension list 2>&1 | grep -E 'ydoc|colab_mcp'"
# Expected: jupyter_server_ydoc enabled OK, jupyter_mcp 0.1.0 OK

# MCP tools via Claude Code
claude mcp list    # Should show: jupyter: http://localhost:8888/mcp (HTTP) - ✓ Connected

Testing Collaboration

To test real-time collaboration, deploy sway-browser-vnc alongside:

ov start sway-browser-vnc
# Open JupyterLab in two Chrome tabs via container DNS:
ov test cdp open sway-browser-vnc "http://ov-jupyter:8888/lab"
# Open second tab
ov test cdp open sway-browser-vnc "http://ov-jupyter:8888/lab"

Executing cells via CDP: Use Input.dispatchKeyEvent (not VNC keys — unreliable when Chrome lacks compositor focus):

TAB=<tab-id>
# Focus cell, then Shift+Enter via CDP
ov test cdp eval sway-browser-vnc $TAB "document.querySelector('.jp-Cell-inputArea .cm-content')?.focus()"
ov test cdp raw sway-browser-vnc $TAB 'Input.dispatchKeyEvent' '{"type":"rawKeyDown","windowsVirtualKeyCode":13,"nativeVirtualKeyCode":13,"modifiers":8}'
ov test cdp raw sway-browser-vnc $TAB 'Input.dispatchKeyEvent' '{"type":"keyUp","windowsVirtualKeyCode":13,"nativeVirtualKeyCode":13,"modifiers":8}'

Test Coverage

Latest ov test jupyter run: 29 passed, 0 failed, 0 skipped. All tests embedded in the org.overthinkos.tests OCI label: jupyter-lab binary under pixi, notebook-templates provisioned into ${HOME}/workspace, jupyter-mcp extension enabled, fastmcp pip package installed. Deploy-scope: supervisord up, port 8888 reachable on 127.0.0.1, /api returns 200 with version in body, /mcp returns 400 on empty POST (proving MCP routing is wired). Image-scope: jupyter_mcp appears in extension list, workspace has ≥1 .ipynb.

See /ov:test for the framework and author-facing gotchas.

Related Skills

• /ov-layers:jupyter, /ov-layers:jupyter-mcp, /ov-layers:notebook-templates
• /ov:test — declarative testing framework
• /ov:config — deploy setup
• /ov:mcp — the image inherits 3 deploy-scope mcp: declarative checks from the jupyter layer (ping, list-tools asserting insert_cell/execute_cell, call list_notebooks). Run ov test jupyter --filter mcp to exercise them against a live deployment, or ov test mcp list-tools jupyter for ad-hoc inspection
• /ov-images:jupyter-ml, /ov-images:jupyter-ml-notebook — GPU variants that inherit the same MCP test suite

When to Use This Skill

MUST be invoked when the task involves the jupyter image, collaborative Jupyter notebooks, lightweight Jupyter deployments without GPU, MCP-based notebook access, or multi-client collaboration. Invoke this skill BEFORE reading source code or launching Explore agents.

Related

• /ov:image — image family umbrella (image: entries in overthink.yml, build/validate/inspect/list)
• /ov:build — build.yml vocabulary (distros, builders, init-systems)