This MCP server allows AI agents within Cursor to interact with Jupyter Notebooks, enabling cell editing, creation, deletion, and metadata management for .ipynb files, overcoming previous limitations.
Added on:
Created by:
Apr 25 2025
This MCP server allows AI agents within Cursor to interact with Jupyter Notebooks, enabling cell editing, creation, deletion, and metadata management for .ipynb files, overcoming previous limitations.
Added on:
Created by:
Apr 25 2025
Jim Beno
Featured
What is Model Context Protocol (MCP) server for Jupyter Notebook?
The MCP server provides a suite of tools for managing Jupyter Notebook files directly within Cursor. It supports creating, deleting, renaming notebooks, reading entire notebooks or specific cells, editing cell content, changing cell types, and exporting notebooks. It uses nbformat to maintain notebook integrity and nbconvert for exporting formats. Designed for data scientists and AI developers, this server simplifies notebook manipulation without kernel execution, ensuring security through path restrictions and size limits, and integrates easily with Cursor environments to enhance notebook workflows.
Who will use Model Context Protocol (MCP) server for Jupyter Notebook?
- Data scientists
- AI developers
- Cursor users working with Jupyter Notebooks
- Python developers managing notebooks programmatically
How to use the Model Context Protocol (MCP) server for Jupyter Notebook?
- Step 1: Install and configure the MCP server following the documentation.
- Step 2: Run the MCP server locally or on a server instance.
- Step 3: Configure Cursor to connect to the MCP server via either stdio or SSE transport.
- Step 4: Use Cursor's interface or API tools to create, read, edit, delete, and export Jupyter Notebook files using the provided MCP functions.
Model Context Protocol (MCP) server for Jupyter Notebook's Core Features & Benefits
The Core Features
- notebook_create
- notebook_delete
- notebook_rename
- notebook_read
- notebook_read_cell
- notebook_add_cell
- notebook_edit_cell
- notebook_delete_cell
- notebook_change_cell_type
- notebook_duplicate_cell
- notebook_get_cell_count
- notebook_read_metadata
- notebook_edit_metadata
- notebook_read_cell_metadata
- notebook_read_cell_output
- notebook_edit_cell_metadata
- notebook_clear_cell_outputs
- notebook_clear_all_outputs
- notebook_move_cell
- notebook_split_cell
- notebook_merge_cells
- notebook_validate
- notebook_get_info
- notebook_export
The Benefits
- Enables direct manipulation of Jupyter Notebooks within Cursor
- Prevents corruption by maintaining notebook structure integrity
- Supports exporting to multiple formats like Python scripts and HTML
- Enforces security with path restrictions and size limits
- Simplifies notebook workflow automation for data science and AI tasks
Model Context Protocol (MCP) server for Jupyter Notebook's Main Use Cases & Applications
- Automated editing and management of Jupyter Notebooks within AI workflows
- Batch creation, deletion, or renaming of notebooks for project organization
- Programmatic reading and modification of notebook cells and metadata
- Exporting notebooks to Python scripts, HTML, or other formats for reporting
- Integrating Jupyter Notebook management into larger AI or data science pipelines
FAQs of Model Context Protocol (MCP) server for Jupyter Notebook
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