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Connect AI agents to control local JupyterLab for coding and analysis.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ipynb-mcp" yet — see the docs or source repo.
Connect ipynb-mcp to my local JupyterLab, open sales_analysis.ipynb, find the failing cell, fix the code, rerun the relevant cells, and summarize the changes.
The AI debugs the notebook, reruns the fixed cells, and provides a summary of changes.
Use ipynb-mcp to open customer_data.ipynb in the current JupyterLab session, inspect the dataset, add missing-value stats, distribution plots, and key findings, then write the results back to the notebook.
An enhanced exploratory analysis is produced with code, charts, and conclusions.
Through ipynb-mcp, control local JupyterLab to open all ipynb files in the experiments folder, run every cell, log failures, and produce a run summary.
You get execution results for each notebook, failure logs, and an overall run summary.
Let AI manage notebook cells, dependencies, and efficient code execution.
Control a live JupyterLab instance from any MCP-compatible client.
Load, edit, search, and save Jupyter notebooks through MCP tools.
Connect and manage Jupyter notebooks for interactive coding, analysis, and visualization.
Read, modify, and batch-process Jupyter notebooks without running a Jupyter server.
Expose Python functions from Jupyter Server as tools for MCP clients.