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Securely control Google Colab notebooks to create, edit, run, and inspect cells.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "colab-mcp" yet — see the docs or source repo.
Connect to my Colab notebook and run all code cells related to data cleaning and summary statistics in order. If any cell fails, stop execution and return the failing cell, error message, and suggested fix.
A summary of execution results, the failed cell location, error details, and actionable fix suggestions.
Create a new code cell in the current Colab notebook, add Python code to train a simple classification model, and run it. If dependencies are missing, add an install cell first, rerun, and report the final metrics.
Creates and runs the required cells, handles dependencies, and outputs training results with key metrics.
Read all cells in this Colab notebook and tell me each cell's type, a brief content summary, recent execution status, and which cells should be merged or refactored.
Provides a notebook structure overview, a cell status list, and maintainability recommendations.
Connect to Colab so AI can run and manage cloud notebooks.
Control Colab notebooks, manage cells, and switch GPUs programmatically.
Connect a local AI agent to Colab for code execution and file operations.
Connect AI agents to control local JupyterLab for coding and analysis.
Control Google NotebookLM end-to-end for research, notes, chat, and exports.
Manage NotebookLM notebooks, sources, chats, and generated artifacts through MCP.