A high-performance Model Context Protocol (MCP) server that brings real-time cybersecurity reconnaissance, attack surface mapping, and automated OSINT telemetry directly to AI agents.
📚 Table of Contents
- Overview
- Features
- Tools & Prompts
- Demo
- Prerequisites
- Installation
- Usage
- Examples
- Registries & Ecosystem
- LLM Client Behavior & Security Considerations
- Legal & Ethical Usage
- Known Limitations
- Project Structure
- Roadmap & Discussions
- Contributing
- License
- Author
🌐 Overview
Modern Large Language Models (LLMs) operate in sandboxed, non-deterministic execution environments devoid of native raw-socket access, packet crafting abilities, and live network reconnaissance primitives. When tasked with auditing an attack surface, AI agents are fundamentally constrained by static training cutoff boundaries and an inability to query live network interfaces.
AynOps bridges this operational divide. Built on top of the Model Context Protocol (MCP) specification using the FastMCP framework, AynOps exposes an extensible suite of deterministic cybersecurity reconnaissance tools directly to LLM agents (such as Cursor, VS Code, Claude Desktop, and custom MCP clients) over a standard JSON-RPC 2.0 stdio transport.






