jupyter-mcp

name	jupyter-mcp
description	JupyterLab CRDT MCP server extension with 11 tools (notebook_*/cell_* + room_list + notebook_list_users) for programmatic notebook access. MUST be invoked when working with: the MCP server implementation, CRDT collaboration, the auto-attach single-room invariant, or the Tier 1 pip-only installation pattern for jupyter extensions.

jupyter-mcp -- JupyterLab CRDT MCP server extension

Layer Properties

Property	Value
Dependencies	(none)
Packages	(none -- pip-only Tier 1 layer)
Services	(none)
Volumes	(none)
Install files	layer.yml, tasks:, jupyter_mcp/ (Python package)

Architecture: Tier 1 Post-Install Layer

This is a Tier 1 "post-install" layer — it has no pixi.toml and installs into whatever pixi environment exists from the parent Tier 2 layer. It follows the same pattern as llama-cpp and unsloth.

Single source of truth: The jupyter_mcp Python package lives only in this layer. Both jupyter (lightweight) and jupyter-ml (GPU ML) compose it via their layers: field. This prevents code duplication and ensures bug fixes propagate to all images.

Usage philosophy and caveats (post-2026-05-06 cutover)

The MCP server manages CRDT rooms invisibly. Clients see notebooks and cells; the server takes care of room lifecycle. Reading or mutating any notebook just works — call cell_get, cell_update, notebook_get directly.

• MCP works WITH the user. Every notebook_* and cell_* tool auto-attaches to whichever CRDT room exists for the path (JupyterLab UI tab, another MCP session, or this one), or creates a fresh room if none exists. Calling cell_update on a notebook the user has open in JupyterLab edits THAT EXACT Y.Doc — the user sees changes in real time. There is no scenario where MCP and UI work in parallel rooms.
• No client-side room management. The pre-cutover room_open / room_close / room_close_all / room_pick tools were deleted. Idle rooms (no clients, no MCP activity for MCP_ROOM_IDLE_TIMEOUT_SEC, default 600s) are flushed and closed by a server-side sweeper. Configure the timeout via env var on the layer for fast tests.
• cell_update is atomic by cell_id. The cell's stable identifier is preserved across the update — pre-cutover versions minted a fresh UUID on every update, producing silent cell duplication when the room had any concurrent state. If you ever observe duplicate cells appearing during a sequence of cell_update calls on a build older than this commit, that's the bug.
• Path canonicalization. Any path you pass — "foo.ipynb", "./foo.ipynb", "/home/user/workspace/foo.ipynb" — is normalized to the workspace-relative form before reaching file_id_manager.index(). All three converge on the same room. Host paths and .. escapes are rejected with a clear error.
• Single room per notebook is an INVARIANT. Two MCP sessions plus the JupyterLab UI editing the same notebook ALL share one Y.Doc. If room_list ever shows two entries for the same logical file, that's a regression — open a bug.
• Don't mix MCP cell-mutations with direct disk writes on the same notebook concurrently. The upstream jupyter_server_ydoc file watcher CRDT-merges external writes against the in-memory state, producing hybrid cells. Pick one writer per session — either MCP or direct file edit, not both.
• After bulk edits, notebook_get cell-count check is cheap insurance. Defensive verification catches upstream regressions early.
• notebook_watch returns on the FIRST change after the call started. Pair with notebook_get if you need to confirm a specific change landed.
• Idle rooms auto-flush + close. A notebook with no connected clients and no MCP activity for MCP_ROOM_IDLE_TIMEOUT_SEC is flushed to disk and removed from memory. The next access auto-creates the room again from disk. No client-driven flush is needed.
• file_id_manager.db is auto-cleaned on adapter init. Rows whose path resolves outside the workspace (host-path leaks) and rows whose underlying file no longer exists (with no active room referencing them) are pruned. Idempotent and safe.

How It Works

The tasks: performs three operations:

1. Install FastMCP — pip install "fastmcp>=3.2.0" (not via pixi because pixi's cross-platform resolver conflicts with opentelemetry-api on aarch64)
2. Install jupyter-mcp extension — pip install --no-deps /ctx/jupyter_mcp (from the layer's build context)
3. Enable Jupyter Server extension — writes jupyter_mcp.json to the Jupyter server config directory

MCP Server

The extension registers a Streamable HTTP MCP server at http://localhost:8888/mcp (MCP spec 2025-11-25). It provides 11 tools, all named in <noun>_<verb> form.

Category	Tools
Notebook management	notebook_list, notebook_create, notebook_get, notebook_watch, notebook_list_users
Cell operations	cell_get, cell_update, cell_insert, cell_delete, cell_execute
Read-only diagnostic	room_list

Tool	Description
notebook_list	List all notebooks in the workspace (filesystem)
notebook_create	Create a new empty notebook on disk (filesystem; the room auto-attaches on the first cell op)
notebook_get	Get full notebook content from the live CRDT document; auto-attaches to existing room or creates one
notebook_watch	Block until a CRDT change is observed; auto-attaches
notebook_list_users	List awareness users currently connected to one notebook's room (read-only diagnostic; returns [] if no room exists)
cell_get	Get a specific cell's content; auto-attaches
cell_update	Replace a cell's source IN PLACE — preserves the cell's id, leaves position in _ycells structurally untouched. Auto-attaches
cell_insert	Insert a new cell at a position; auto-attaches
cell_delete	Delete a cell; auto-attaches
cell_execute	Execute a cell, return outputs, AND write outputs + execution_count back via the in-place set_cell path. Auto-attaches
room_list	Read-only diagnostic: list every active CRDT room with rich metadata (room_id, path, file_id, users, user_count, has_kernel). Use to verify the single-room invariant

CRDT Collaboration

Cell operations mutate the live CRDT document — changes appear instantly in all connected JupyterLab clients. Multiple MCP clients and browser users can edit the same notebook simultaneously. The server uses jupyter-server-ydoc for CRDT document management. The single-room invariant means MCP and UI clients ALWAYS share one Y.Doc per logical file — calls converge through deterministic room_id = json:notebook:<file_id_manager.index(canonical_path)>.

Key implementation details (post-2026-05-06):

1. Auto-attach in _resolve_notebook_doc. Every path-accepting adapter method routes through this single resolve point. It computes the canonical path, looks up the deterministic room_id, and either attaches to the existing server.rooms[room_id] (UI-created or MCP-created) or constructs a new room mirroring YDocWebSocketHandler.prepare(). Same code path → same room → no parallel state.

2. In-place set_cell. Cell mutations operate on the existing Y.Map's source (Y.Text), metadata (Y.Map), and outputs (Y.Array) IN PLACE. The cell's id and its position in _ycells are structurally untouched. Compare with the pre-cutover code that delegated to upstream YNotebook.set_cell, which calls create_ycell(value) (mints a fresh UUID when value lacks id) and set_ycell(index, ycell) (a pycrdt.Array.__setitem__ that decomposes into delete-then-insert at the CRDT level — phantom-cell residue). A post-condition verify ensures id stability after every mutation.

3. Path canonicalization at the boundary. _canonical_notebook_path(path) resolves host paths and .. escapes BEFORE any call to file_id_manager.index(). Prevents the 2026-05 host-path-leak bug pattern where /home/atrawog/... got minted into the container's file_id manager from a stray client call.

4. Server-side idle-room sweeper. A background asyncio task runs every MCP_ROOM_SWEEP_INTERVAL_SEC (default 60), flushes and closes rooms with zero connected clients and idle for > MCP_ROOM_IDLE_TIMEOUT_SEC (default 600). Activity tracking via _room_last_active: every CRDT mutation through the adapter bumps the timestamp, so an actively-mutated MCP-only room (no WS clients) is not reaped.

5. cell_execute persists outputs. After collecting iopub messages, the adapter translates them via nbformat.v4.output_from_msg, captures execution_count from the shell-channel execute_reply (works for print()/display()-only cells that don't emit execute_result), and writes cell["outputs"] + cell["execution_count"] back via set_cell() — the same in-place CRDT-aware path used by cell_update. Eventually flushed to disk by the upstream save_delay or the idle-room sweeper.

6. file_id_manager.db cleanup on init. A one-shot pass deletes rows whose path is outside the notebook root (host-path leaks) or whose underlying file no longer exists (with no active room referencing them). Idempotent — safe to run on every server start.

Architecture Stack

Claude Code / MCP Client
    ↓ Streamable HTTP (POST /mcp)
FastMCP Server (mcp_server.py)            — 11 tools, no room_* management
    ↓
JupyterLab RTC Adapter (rtc_adapter.py)   — auto-attach + canonicalization
    ↓ CRDT operations (in-place Y.Map mutation)
jupyter-server-ydoc DocumentRoom          — single room per file_id
    ↓ Y.js document sync
JupyterLab Kernel Manager (execute_cell)

Key Files

layers/jupyter-mcp/
  layer.yml              # Description only (Tier 1, no deps)
  # tasks: block in layer.yml — fastmcp + pip install + extension enable
  jupyter_mcp/           # Python package
    pyproject.toml
    jupyter_mcp/
      __init__.py
      app.py             # Jupyter server extension entry point
      mcp_server.py      # FastMCP tool definitions (11 tools)
      rtc_adapter.py     # CRDT room management, kernel execution
      tornado_asgi.py    # Tornado-to-ASGI bridge for FastMCP

Integration with mcp_provides

The parent jupyter layer declares mcp_provides to make this MCP server discoverable to other services at deploy time. The hermes service auto-discovers this server via the OV_MCP_SERVERS env var and registers all 11 tools as mcp_jupyter_<tool_name>.

MCP Name Decoupling (design principle)

The jupyter MCP server name is deliberately decoupled from the layer name, the Python package name, and the image name. Three places anchor it:

File	Field	Value	Purpose
layers/jupyter/layer.yml	env.MCP_SERVER_NAME	"jupyter"	Runtime advertisement
layers/jupyter/layer.yml	mcp_provides[0].name	jupyter	Cross-container discovery (hermes, openwebui)
plugins/ov-jupyter/.mcp.json	mcpServers.jupyter	—	Claude Code static registration

Package/layer/image names describe the artifact; the MCP name describes the service contract. Rename the artifact freely; the contract is stable.

Used In Layers

• /ov-jupyter:jupyter — lightweight multi-arch JupyterLab (layers: [jupyter-mcp])
• /ov-jupyter:jupyter-ml — GPU ML JupyterLab (layers: [llama-cpp, unsloth, jupyter-mcp])

Used In Images

• /ov-jupyter:jupyter
• /ov-jupyter:jupyter-ml
• /ov-jupyter:jupyter-ml-notebook

Related Skills

• /ov-jupyter:jupyter — lightweight Tier 2 parent layer
• /ov-jupyter:jupyter-ml — GPU ML Tier 2 parent layer
• /ov-build:layer — layer authoring rules (Tier 1 pattern)
• /ov-build:mcp — client-side verb for probing this server's tool catalog (ping, list-tools, call); use ov eval mcp list-tools jupyter to see all 11 tools this layer registers
• /ov-selkies:chrome-devtools-mcp — sibling MCP-server-provider layer for Chrome DevTools (different domain, same mcp_provides pattern)
• /ov-hermes:hermes — downstream MCP consumer (auto-discovers jupyter via OV_MCP_SERVERS; uses the 11 tools to read/edit/execute notebook cells programmatically)
• /ov-openwebui:openwebui — downstream MCP consumer (sets CODE_EXECUTION_ENGINE=jupyter when this server is discovered, routing Open WebUI's in-chat code blocks to the Jupyter kernel)

When to Use This Skill

Use when the user asks about:

• The jupyter-mcp layer or its MCP server implementation
• How the CRDT collaboration works in JupyterLab
• The 11 MCP tools for notebook manipulation
• The auto-attach single-room invariant
• The path canonicalization rules
• The idle-room sweeper / cleanup behavior
• How the MCP extension is installed and enabled
• The Tier 1 extraction pattern (avoiding code duplication across Tier 2 layers)

Related

• /ov-build:eval — declarative testing (eval: block, ov eval image, ov eval live)