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/commands/datalayer/jupyter-mcp-server/run)<a href="https://agentmods.dev/commands/datalayer/jupyter-mcp-server/run"><img src="https://agentmods.dev/badge/commands/datalayer/jupyter-mcp-server/run/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/commands/datalayer/jupyter-mcp-server/run"><img src="https://agentmods.dev/badge/commands/datalayer/jupyter-mcp-server/run.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00234 |
| Opus 5.5 | $0.00000 | $0.00094 |
| Sonnet 5.5 | $0.00000 | $0.00047 |
| Haiku 4.5 | $0.00000 | $0.00023 |
Grade A, and why
run 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
description: Run a cell, or the whole notebook, on Datalayer argument-hint: [cell index, "all", or a description]
Execute code in the Datalayer notebook we are working in.
Execution happens on the server, so it keeps running whether or not this session stays open. Say so when a computation looks long.
Steps:
- If no notebook is active, run
/datalayer:notebookfirst. - Work out what
$ARGUMENTSmeans:- a number — execute that cell with
execute_cell; all— read the notebook, then execute the code cells in order;- anything else — find the cell that matches the description, show it, and confirm before running it.
- a number — execute that cell with
- Report what each cell produced. If a cell fails, show the error and offer a fix rather than retrying blindly.
Never edit a cell to make it pass without saying what you changed and why.
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 · 29 lines · 0 tokens per session scan A 4476803905de
run is a command published in the GitHub repository datalayer/jupyter-mcp-server (1,317 stars, last pushed 5d ago), licensed BSD-3-Clause. It costs nothing until one of its globs matches a file; then it loads 234 tokens. 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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ingest-dev
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resume
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scan-secrets
Scan the current project for hardcoded secrets and offer to auto-fix them.
next
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