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213,242 tools. Last updated 2026-06-19 14:30

"A tool for executing code in Jupyter Notebooks" matching MCP tools:

  • Scrape NotebookLM homepage to retrieve notebook IDs and names. Discover, verify, or clean up notebooks in your account.
    MIT
  • Connect to a remote Jupyter server to run code on remote compute, GPUs, or data while keeping notebooks saved locally.
    MIT
  • Generate and customize Jupyter notebooks for Python and Markdown tasks, ensuring unique names and valid JSON content for streamlined data analysis and integration with SingleStore databases.
    MIT
  • Export Databricks workspace objects (notebooks or files) in formats like SOURCE, HTML, JUPYTER, DBC, or RAW. Returns decoded text where possible.
    MIT

Matching MCP Servers

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    Enables AI agents to interact with Jupyter notebooks via MCP tools for querying, modifying, executing, and setting up notebooks, with state preservation and real-time collaboration.
    Last updated
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    Apache 2.0

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  • Execute code directly in Jupyter notebooks for debugging, profiling, and temporary calculations without saving to the notebook.
    BSD 3-Clause
  • Retrieve all available Jupyter kernel sessions with IDs, names, states, and specifications to monitor resources and identify kernels for connection.
    BSD 3-Clause
  • List all Jupyter notebook files (.ipynb) in the current working directory. Quickly see available notebooks for selection or management.
    MIT
  • Execute Python code in a persistent sandbox environment to run computations, analyze data, or build multi-tool workflows while maintaining state across executions.
    GPL 3.0
  • Estimate token cost and latency for executing code by analyzing historical samples of similar call patterns.
    MIT
  • Upload local Jupyter notebooks to Microsoft Fabric workspaces for data analysis and engineering workflows. Transfer .ipynb files with optional folder organization and descriptions.
    MIT
  • Modify Jupyter notebook cells by replacing, inserting, or deleting content to update code and documentation in data analysis workflows.
    MIT
  • Index or update a knowledge directory to enable semantic search across markdown, Python, and Jupyter notebook files. Specify the directory path and optionally control recursion and reindexing.
    MIT
  • List Jupyter notebooks in any directory to quickly browse available notebooks and manage your research workflow.
    MIT