MCP Jupyter
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# Jupyter MCP Server > **⚠️ API Compatibility Notice**: This project is currently focused on MCP (Model Context Protocol) usage. There are **no API compatibility guarantees** between versions as the interface is actively evolving. Breaking changes may occur in any release. Jupyter MCP Server allows you to use tools like [Goose](https://block.github.io/goose/) or Cursor to pair with you in a JupyterLab notebook where the state of your variables is preserved by the JupyterLab Kernel. This enables seamless collaboration where agents can install packages, fix errors, and hand off to you for data exploration at any time. **Architecture**: Uses Jupyter's REST API for reliable agent operations while maintaining real-time user synchronization through RTC. See [Architecture Documentation](docs/docs/architecture.md) for detailed technical information. ## Key Features - **4 Consolidated MCP Tools** (reduced from 11): - `query_notebook` - All read-only operations (view source, check server, etc.) - `modify_notebook_cells` - All cell modifications (add, edit, delete cells) - `execute_notebook_code` - All execution operations (run cells, install packages) - `setup_notebook` - Notebook initialization and kernel connection - **Workflow-oriented design** optimized for AI agent collaboration - **State preservation** across notebook sessions - **Automatic parameter validation** with float-to-int conversion This works with any client that supports MCP but will focus on using Goose for the examples. ## Requirements You will need [UV](https://docs.astral.sh/uv/) is required to be installed. ## Installation This MCP server supports multiple transport modes and can be added to client with the command `uvx mcp-jupyter`. ### Transport Modes The server supports two transport protocols: - **stdio** (default) - Standard input/output communication, ideal for local IDE integrations - **http** - Streamable HTTP transport with session management, enabling serverless deployments a…
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