Jupyter MCP Server is a Model Context Protocol server that lets AI agents connect to and manage Jupyter notebooks in real time. It supports notebooks running locally or on hosted platforms, and its catalogue add-ons provide commands and skills for working with those notebooks.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/datalayer/jupyter-mcp-serverWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/plugins/datalayer/jupyter-mcp-server/claude-plugin)<a href="https://agentmods.dev/plugins/datalayer/jupyter-mcp-server/claude-plugin"><img src="https://agentmods.dev/badge/plugins/datalayer/jupyter-mcp-server/claude-plugin/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/plugins/datalayer/jupyter-mcp-server/claude-plugin"><img src="https://agentmods.dev/badge/plugins/datalayer/jupyter-mcp-server/claude-plugin.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
datalayer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
{
"name": "datalayer",
"description": "Connect Claude Code to Datalayer Notebooks — always-on notebooks, durable execution, and your data, through the hosted Jupyter MCP Server.",
"version": "0.1.0",
"author": {
"name": "Datalayer",
"email": "[email protected]",
"url": "https://datalayer.ai"
},
"homepage": "https://datalayer.ai/docs/access-jupyter-mcp-server",
"repository": "https://github.com/datalayer/jupyter-mcp-server",
"license": "BSD-3-Clause",
"keywords": ["jupyter", "notebook", "datalayer", "mcp", "data"]
}
What it installs
The manifest is a name and a version. 3 commands travel with it, and installing the plugin installs all of them — 0 tokens a session between them. Each is measured on its own page, and each can be installed alone.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 15 lines scan A a9a42f6249e9
datalayer is a plugin published in the GitHub repository datalayer/jupyter-mcp-server (1,317 stars, last pushed 5d ago), licensed BSD-3-Clause. Its token cost is not measured: this kind of file is read by the harness, not the model. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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