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Run Jupyter notebooks on temporary AWS compute with cost estimates and cleanup.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "AWS Notebook Runner MCP" yet — see the docs or source repo.
Run this Jupyter notebook on a temporary AWS EC2 environment. First perform a dry-run plan and estimate the total cost, then execute it. Report progress continuously during execution and automatically clean up the instance and related resources when finished.
Provides an execution plan, estimated cost, live progress updates, and final results with cleanup status.
Use AWS Notebook Runner to run a training notebook on temporary compute, verify dependencies, confirm the resource size is appropriate, and output both the dry-run results and execution logs. Automatically release resources after the job finishes.
Returns environment validation results, resource planning guidance, training logs, and cleanup confirmation.
Help me batch-run multiple reporting Jupyter notebooks in a temporary AWS environment. First estimate the cost for each task and the total cost, then run them sequentially with progress updates for each notebook, and finally clean up all resources automatically.
Outputs per-notebook cost estimates, execution status, result summaries, and a consolidated cleanup report.
Connect and manage Jupyter notebooks for interactive coding, analysis, and visualization.
Create and manage AWS infrastructure resources through an MCP server.
Manage NotebookLM notebooks, sources, chats, and generated artifacts through MCP.
Load, edit, search, and save Jupyter notebooks through MCP tools.
Let AI assistants securely call AWS APIs with seamless SSO re-login.
Provision and terminate AWS EC2 instances with natural language for simpler cloud operations.