Borrowing it
Nothing to install: this file belongs to 0x0L/ipykernel-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/0x0L/ipykernel-mcp/main/AGENTS.mdgit clone --depth 1 https://github.com/0x0L/ipykernel-mcpWrote 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/instructions/0x0l/ipykernel-mcp/agents-md)<a href="https://agentmods.dev/instructions/0x0l/ipykernel-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/0x0l/ipykernel-mcp/agents-md/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/instructions/0x0l/ipykernel-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/0x0l/ipykernel-mcp/agents-md.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 | $0.01269 | $0.01269 |
| Opus 5 | $0.00634 | $0.00634 |
| Sonnet 5 | $0.00254 | $0.00254 |
| Haiku 4.5 | $0.00127 | $0.00127 |
Grade A, and why
ipykernel-mcp AGENTS.md 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repository guidance
Purpose and documentation
Help an MCP agent work incrementally: retain loaded data, reuse functions and
intermediate results, inspect local images, and refine plots across calls. The
README covers human-facing setup and usage; docs/usage.md contains example
conversations. Keep tool-call protocols, polling, and result-consumption details
in MCP server instructions, tool docstrings, and schemas, not the README.
Keep examples aligned with the six-tool contract and identify optional libraries
that must be installed in the configured kernel environment. Image interpretation
depends on the client's support for image tool results and the model's vision
capabilities. Distinguish session state from retained output and saved files.
The model receives server instructions, tool docstrings, and JSON schemas; do not
rely on it reading the README. Put the essential execute/read/consume workflow at
the beginning of server instructions. Each tool description should state when to
use it, what it returns or changes, and the next action for running/error outcomes.
Describe parameters and result fields with Field(description=...), including
null/empty meanings and configured versus live values. Keep tool descriptions
self-contained and examples executable. Verify metadata through a real MCP client,
including nested schema fields, rather than testing source wording alone.
Product contract
One configured Jupyter kernelspec, one persistent Jupyter kernel, one active execution.
--jupyter and --kernel are required; --cwd defaults to the server's working directory. The server
opens that kernel in its lifespan and closes it on shutdown. Configuration cannot change
through MCP tools. There is no environment discovery or public start/stop operation.
The six tools are execute, read_output, drain_output, interrupt, restart,
and status.
Keep names aligned between the README, MCP tools, and Python methods. restart clears
the Jupyter kernel. Returned output is consumed, including execute responses.
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.
- 7d ago First seen · 110 lines · 1,269 tokens per session scan A 70f5f53a2b59
ipykernel-mcp AGENTS.md is an instructions file published in the GitHub repository 0x0L/ipykernel-mcp (1 stars, last pushed 7d ago), licensed MIT. It adds 1,269 tokens to every session, about $0.0063 per session on Opus 5. 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 2026-09-07.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.