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Load, edit, search, and save Jupyter notebooks through MCP tools.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "jupyter-notebook-mcp" yet — see the docs or source repo.
Open this .ipynb file, list all cells with their indices, then move the data-cleaning code cells under the 'Feature Engineering' heading and save the notebook.
Returns the current cell structure, explains the updated indices, and saves the modified notebook file.
Search the current Jupyter notebook for all cells that read CSV files, add encoding='utf-8' to pandas.read_csv, and show the updated results.
Identifies relevant code cells, applies the batch update, and shows the revised code snippets.
Load this experiment notebook, summarize the purpose of each Markdown and code cell in order, and flag parts missing explanations or result interpretation.
Provides a structured review summary and highlights documentation gaps for follow-up editing and sharing.
Connect and manage Jupyter notebooks for interactive coding, analysis, and visualization.
Connect to Jupyter via MCP to run code and explore data interactively.
Let AI read, edit, and execute Jupyter notebooks directly.
Connect AI agents to control local JupyterLab for coding and analysis.
Wraps the nb CLI for AI-friendly note creation, organization, and retrieval.
Control Google NotebookLM end-to-end for research, notes, chat, and exports.