Borrowing it
Nothing to install: this file belongs to complyue/jupyter-collaboration-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/complyue/jupyter-collaboration-mcp/main/AGENTS.mdgit clone --depth 1 https://github.com/complyue/jupyter-collaboration-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/complyue/jupyter-collaboration-mcp/agents-md)<a href="https://agentmods.dev/instructions/complyue/jupyter-collaboration-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/complyue/jupyter-collaboration-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/complyue/jupyter-collaboration-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/complyue/jupyter-collaboration-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 | $0.01965 | $0.01965 |
| Opus 5 | $0.00983 | $0.00983 |
| Sonnet 5 | $0.00393 | $0.00393 |
| Haiku 4.5 | $0.00197 | $0.00197 |
Grade A, and why
jupyter-collaboration-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 9d 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 — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agent Guide for Jupyter Collaboration MCP Server
This guide provides AI agents with the essential information needed to understand, contribute to, and work effectively with the Jupyter Collaboration MCP Server project.
Project Overview
The Jupyter Collaboration MCP Server is a JupyterLab extension that provides MCP (Model Context Protocol) server endpoints to expose Jupyter Collaboration's real-time collaboration (RTC) functionalities to AI agents. This project enables AI agents to interact with collaborative Jupyter notebooks and documents in real-time, leveraging the existing Jupyter Collaboration system's robust RTC capabilities using YDoc (CRDT) technology.
Key Concepts for AI Agents
- MCP (Model Context Protocol): The communication standard between AI agents and the server
- RTC (Real-Time Collaboration): The underlying technology enabling multiple users to collaborate simultaneously
- YDoc: CRDT (Conflict-free Replicated Data Type) technology used for synchronization
- Jupyter Collaboration: The existing system providing collaborative features for Jupyter notebooks
Project Structure
Based on the design document, the project follows this structure:
jupyter-collaboration-mcp/
├── pyproject.toml
├── setup.py
├── jupyter_collaboration_mcp/
│ ├── __init__.py
│ ├── app.py # Main MCP server application
│ ├── handlers.py # MCP request handlers
│ ├── rtc_adapter.py # Adapter to existing RTC functionality
│ ├── event_store.py # For resumability
│ ├── auth.py # Authentication and authorization
│ └── utils.py # Utility functions
└── tests/
├── __init__.py
├── test_app.py
├── test_handlers.py
└── test_auth.py
Key Components
1. MCP Server (StreamableHTTP)
- Located in
app.py - Uses HTTP with Server-Sent Events (SSE) for real-time communication
- Based on the MCP StreamableHTTP example
2. RTC Adapter Layer
- Located in
rtc_adapter.py - Translates MCP requests into operations on the existing collaboration system
- Bridges the gap between MCP protocol and Jupyter Collaboration
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.
- 9d ago First seen · 261 lines · 1,965 tokens per session scan A 6d34b1b722e9
jupyter-collaboration-mcp AGENTS.md is an instructions file published in the GitHub repository complyue/jupyter-collaboration-mcp (0 stars, last pushed 1y ago), licensed MIT. It adds 1,965 tokens to every session, about $0.0098 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 2026-08-31.
Other instructions, from other repositories
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 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).
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).
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.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.