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Compare scores, permissions, risks, and fit in one decision-focused table.

DimensionJupyter MCP ServerAn MCP server for AI to connect and manage Jupyter Notebooks in real timeGitHub MCP ServerGitHub's official MCP server for managing repos, issues, PRs, and workflows via natural language
FMRS65 / 100 · C76 / 100 · B
Reliability10 / 2013 / 20
Security and permissions12 / 2014 / 20
Maintenance13 / 2018 / 20
Documentation15 / 2017 / 20
Setup experience15 / 2014 / 20
Best for
  • Users already running a local JupyterLab or JupyterHub who want to attach an AI agent
  • Data science and machine learning workflows needing multimodal output (images, plots, text)
  • Teams wanting to extend code execution from local to cloud sandboxes such as Datalayer, Kaggle or Google Colab
  • Users who want notebook sessions to keep running after the agent disconnects
  • Teams that want an AI assistant to directly operate on GitHub repos and collaboration workflows
  • Users already in the GitHub Copilot ecosystem who want a zero-deployment remote option
Not for
  • Users who do not run any Jupyter Server and do not need Jupyter Notebooks
  • Users whose LLM or client cannot handle multimodal image output and who do not want to disable ALLOW_IMG_OUTPUT
  • Users who need cloud sandbox variants without network access or the required credentials
  • Scenarios where you don't want the assistant to have write access to repos (enable only read-only toolsets)
  • Environments with strict network isolation for private repos that can't reach the official remote endpoint
Required permissions
  • Access to the Jupyter Server URL (JUPYTER_URL / DOCUMENT_URL / CODE_SANDBOX_URL)
  • The Jupyter access token (JUPYTER_TOKEN, or DOCUMENT_TOKEN and CODE_SANDBOX_TOKEN separately)
  • Permission to read and write notebook documents and execute code
  • Vendor credentials when using cloud sandboxes (e.g. DAYTONA_API_KEY, E2B_API_KEY, CWSANDBOX_API_KEY, CLOUDFLARE_SANDBOX_API_URL / CLOUDFLARE_SANDBOX_API_KEY, Kaggle credentials, Modal credentials)
  • Optional OAuth 2.1 scopes with Datalayer hosting: notebooks:read, notebooks:write, code:execute, data:read
  • A personal access token (PAT) or OAuth App token; effective scope depends on the token's own permissions
  • Enabling toolsets like actions/issues/pull_requests grants write access — request tokens on a least-privilege basis
Risks and side effects
  • The AI agent can execute arbitrary code in notebooks, which may lead to data leakage, data corruption or resource abuse
  • A leaked token grants the same Jupyter access as the token itself
  • Starting JupyterLab with --ip 0.0.0.0 by default widens the network exposure surface
  • Cloud sandboxes incur vendor-side costs and may run beyond local resource limits
  • A version mismatch between jupyter-mcp-server and code-sandboxes fails on first execution with Unknown sandbox variant: jupyter
  • Write toolsets (creating/merging PRs, triggering workflows) can cause accidental changes if the token is overscoped — try a read-only toolset first
  • In hosted mode, credentials travel via the Authorization header — make sure the client-to-api.githubcopilot.com connection is trusted
Supported clientsClaude Desktop, Cursor, Windsurf, VS Code, Cline, Claude CodeClaude Desktop, Claude Code, VS Code, Cursor, Windsurf, JetBrains IDEs, Zed, Amp
Tools615