Scrape NotebookLM homepage to retrieve notebook IDs and names. Discover, verify, or clean up notebooks in your account.
MIT
213,242 tools. Last updated 2026-06-19 14:30
"A tool for executing code in Jupyter Notebooks" matching MCP tools:
- Query Jupyter notebook information and metadata, including source code, server status, sessions, and cell positions.Apache 2.0
- Add, edit, or delete code and markdown cells in Jupyter notebooks, with optional immediate execution.Apache 2.0
- 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
- AlicenseAqualityDmaintenanceEnables 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 updated442Apache 2.0
- Alicense-qualityBmaintenanceA configurable MCP server extension for Jupyter Server that enables dynamic registration of Python functions as tools accessible to MCP clients from a running Jupyter Server.Last updated21BSD 3-Clause
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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
- Execute code cells or install packages in a Jupyter notebook. Requires prior notebook setup to specify the server URL.Apache 2.0
- Synchronize your local NotebookLM library by comparing it with actual notebooks, identifying stale entries, and discovering missing notebooks for accurate data management.MIT
- 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
- Run Python code in a persistent Jupyter kernel, preserving variables and imports across executions.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
- Locate Jupyter notebooks by filename, even when the full path is unknown.MIT
- Retrieve a list of all Jupyter notebooks previously accessed through the server's notebook interaction tools.BSD 3-Clause