MCP Jupyter Server
Related Servers
Alternatives to MCP Jupyter Server
No user-submitted related servers found.
Related Servers
- FlicenseNot gradedqualityDmaintenanceEnables AI agents to safely and structurally edit Jupyter Notebook (.ipynb) files by providing tools to read, edit, add, and delete cells without corrupting the JSON structure.1-
- FlicenseAqualityDmaintenanceProvides tools for interacting with Jupyter notebooks, allowing users to read, add, and execute notebook cells. It supports full notebook execution and metadata retrieval through the FastMCP framework.52-
- AlicenseAqualityDmaintenanceEnables AI assistants to read, edit, and execute Jupyter notebook files (.ipynb) with live kernel output, without requiring JupyterLab.17MIT
- AlicenseNot gradedqualityBmaintenanceMCP server for editing Jupyter notebooks (.ipynb) with 23 cell-level tools: create, read, search and restructure cells, manage outputs and metadata, and execute cells on a live Jupyter kernel (python3 built in; R, Julia via installed kernels). Built on nbformat and jupyter_client.MIT
- AlicenseAqualityDmaintenanceEnables programmatic interaction with Jupyter notebooks, allowing reading, editing, and executing cells via Claude.646 PyPI34MIT
- FlicenseAqualityCmaintenanceEnables adding and editing cells in local Jupyter Notebooks, and executing individual cells on the notebook's specified kernel, with state persisted across cells.6-
TDQS
Scored across 13 tools
Most tools have distinct purposes for cell operations, but there is notable overlap between 'edit_cell' and 'edit_cell_source' which both edit cell source code, and between 'add_cell' and 'insert_cell' which both add cells. This could cause confusion for an agent trying to select the right tool.
Tool names follow a consistent verb_noun pattern (e.g., add_cell, delete_cell, execute_cell) with clear actions and targets. The only deviation is 'read_notebook_with_outputs' which is longer but still follows the pattern, and 'trigger_vscode_reload' which uses a verb_noun_noun structure but remains readable.
With 13 tools, this server is well-scoped for managing Jupyter notebooks, covering a comprehensive set of operations like adding, editing, executing, and listing cells. Each tool appears to serve a specific function without unnecessary bloat.
The toolset provides strong coverage for cell-level operations (CRUD, execution, type conversion) and notebook reading, but lacks tools for higher-level notebook management such as saving, creating new notebooks, or kernel control. Minor gaps exist, but agents can likely work around them.