This release is a pre-release and may not be stable for production use.
nteract
A convenience wrapper that finds and launches runt mcp — the MCP server shipped with the nteract desktop app.
Download the nteract desktop app — you'll need it to see notebooks, collaborate with agents, and manage environments.
The recommended way to add the MCP server is
runt mcpdirectly. This PyPI package is a convenience for users who preferuvx.
Quick Start
The MCP server ships with the desktop app. After installing, use runt mcp directly:
Claude Code
# Stable
claude mcp add nteract -- runt mcp
# Nightly
claude mcp add nteract-nightly -- runt-nightly mcp
Manual JSON config
{
"mcpServers": {
"nteract": {
"command": "runt",
"args": ["mcp"]
}
}
}
Via this PyPI package
If runt isn't on your PATH, this package finds it in the app bundle:
claude mcp add nteract -- uvx nteract
That's it. Now Claude can execute Python code, create visualizations, and work with your data.
What is this?
nteract is an MCP (Model Context Protocol) server that connects AI assistants like Claude to Jupyter notebooks. It enables:
- Code execution: Run Python in a persistent kernel
- Real-time collaboration: Watch the AI work in the nteract desktop app
- Shared state: Multiple agents can work on the same notebook
- Environment management: Automatic Python environment setup
Example
Ask Claude:
"Help me visualize my log data"
Claude will:
- Connect to a notebook session
- Write and execute code
- Generate visualizations
- Show you the results
You can open the same notebook in the nteract desktop app to see changes in real-time and collaborate with the AI.
Available Tools
| Tool | Description |
|---|---|
list_active_notebooks |
List all open notebook sessions |
join_notebook |
Join an existing notebook session by ID |
open_notebook |
Open an existing .ipynb file |
create_notebook |
Create a new notebook |
save_notebook |
Save notebook to disk as .ipynb file |
create_cell |
Add a cell to the notebook (use and_run=True to execute) |
execute_cell |
Run a specific cell (returns partial results after timeout) |
run_all_cells |
Queue all code cells for execution |
set_cell |
Update a cell's source and/or type |
get_cell |
Get a cell by ID with outputs |
get_all_cells |
View all cells in the notebook |
replace_match |
Targeted literal text find-and-replace in a cell |
replace_regex |
Regex-based find-and-replace in a cell |
move_cell |
Reorder a cell within the notebook |
clear_outputs |
Clear a cell's outputs |
delete_cell |
Remove a cell from the notebook |
interrupt_kernel |
Interrupt the currently executing cell |
restart_kernel |
Restart kernel with updated dependencies |
show_notebook |
Open the notebook in the nteract desktop app (disabled with --no-show) |
add_dependency |
Add a Python package dependency |
remove_dependency |
Remove a dependency |
get_dependencies |
List current dependencies |
sync_environment |
Hot-install new deps without restart |
CLI Flags
| Flag | Description |
|---|---|
--version |
Print version and exit |
--nightly |
Use runt-nightly (nightly daemon and app) |
--stable |
Use runt (stable daemon and app, default) |
--legacy |
Use the built-in Python MCP server instead of runt mcp |
By default, nteract finds and exec's the installed runt (or runt-nightly) binary. The --legacy flag falls back to the built-in Python MCP server.
Architecture
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Claude │────▶│ nteract │────▶│ runtimed │
│ (or other │ │ MCP Server │ │ daemon │
│ AI) │ │ │ │ │
└─────────────┘ └─────────────┘ └──────┬──────┘
│
┌─────────────┐ │
│ nteract │◀────────────┘
│ Desktop App │ (real-time sync)
└─────────────┘
- nteract (this package): MCP server for AI assistants
- runtimed: Low-level daemon and Python bindings (docs)
- nteract desktop: Native app for humans to collaborate with AI
Real-time Collaboration
The magic of nteract is that AI and humans share the same notebook:
- AI connects via MCP and runs code
- Human opens the same notebook in nteract desktop
- Changes sync instantly via CRDT
- Both see the same kernel state
This enables workflows like:
- AI does initial analysis, human refines
- Human writes code, AI debugs errors
- Multiple AI agents collaborate on complex tasks
Development
# Clone
git clone https://github.com/nteract/desktop
cd desktop/python/nteract
# Install dependencies
uv sync
# Run tests
uv run pytest
Related Projects
- nteract/desktop - Native desktop app
- runtimed on PyPI - Low-level Python bindings
License
BSD-3-Clause
1 maintainer
Metadata
1 maintainer
Release files for nteract 2.1.1a202604050218
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