Simulation plays a key role in the design and evaluation of distributed systems, yet it is often treated as a static tool with limited interaction capabilities. In this work, we present Yet (not) Another Intelligent Fog Simulator (YAIFS), and evolution of YAFS that redefines simulation as an interactive, service-oriented environment. YAIFS introduce a layered architecture that exposes the simulation through a unified API and service interface, enabling external entities to observe, control, and modify its execution. A central contribution is the integration of the Model Context Protocol (MCP) as a standardized interaction layer between agents and the simulation. Through MCP, heterogeneous agents can access state, invoke actions and coordinate behavior using a common set of tools, decoupling agent experimentation workflows. We illustrate these capabilities through two scenarios: an LLM-based assistant that enable natural language control of simulations, and a multi-agent setting where agents monitor system conditions and adapt placement decisions at runtime. These scenarios demonstrate how MCP structures agent-simulation interaction and enable adaptive behavior under dynamic workloads. The proposed approach transforms simulation into an interactive and programmable environment, opening new directions for AI-driven experimentation in cloud-edge systems. The implementation is publicly available at: http://github.com/acsicuib/YAIFS

翻译：仿真在分布式系统的设计与评估中扮演关键角色，但常被视作交互能力有限的静态工具。本文提出"又一个（并非）智能雾计算模拟器"（YAIFS）——基于YAFS的演进版本，重新将仿真定义为交互式、面向服务的环境。YAIFS引入分层架构，通过统一API与服务接口暴露仿真过程，使外部实体能够观察、控制并修改其执行过程。核心贡献在于集成模型上下文协议（MCP）作为智能体与仿真的标准化交互层。通过MCP，异构智能体可访问状态、调用动作并使用通用工具集协调行为，从而解耦智能体实验工作流。我们通过两个场景展示这些能力：基于大语言模型（LLM）的助手实现仿真的自然语言控制，以及多智能体场景中智能体监控系统条件并动态调整部署决策。这些场景证明了MCP如何结构化智能体-仿真交互，并支持动态工作负载下的自适应行为。所提方法将仿真转化为交互式可编程环境，为云-边缘系统中AI驱动实验开辟新方向。实现代码已开源于：http://github.com/acsicuib/YAIFS