Lawrence Berkeley National Laboratory

Traditional building energy modelling workflows remain labor-intensive and error-prone, requiring specialized expertise that limits broader adoption. This paper introduces a novel Model Context Protocol (MCP)-enabled framework that connects AI assistants to EnergyPlus through MCP, a standardized interface for tool invocation and context management. Two complementary integration paradigms are presented and compared: conversational integration, where users interact through natural language while an AI assistant orchestrates MCP tools on demand, and agentic workflow integration, where specialized agents coordinate autonomously to complete multi-step tasks. Using an experimental testbed for residential buildings, the end-to-end workflows are demonstrated. The conversational approach reduced typical inspection and modification tasks from 1-2 h to under 15 min, while maintaining full transparency through visible tool invocations. The agentic approach automated parametric analysis. These demonstrations establish MCP as a foundational layer for AI-assisted building energy modelling, enabling natural language interactions with simulation tools while preserving professional oversight and decision-making authority. Highlights An MCP-enabled framework provides standardized integration between AI assistants and EnergyPlus for building energy modelling workflows.Two complementary paradigms are demonstrated: conversational integration for exploratory tasks and agentic workflow integration for systematic automation.End-to-end demonstrations on a residential energy model show an 80% to 90% time reduction for model inspection, modification, and analysis tasks.The framework augments rather than replaces professional judgment, with AI handling tool orchestration while practitioners retain decision-making authority.The approach establishes a foundational infrastructure for advanced capabilities including parametric simulations, BIM-to-BEM translation, and automated model calibration. An MCP-enabled framework provides standardized integration between AI assistants and EnergyPlus for building energy modelling workflows. Two complementary paradigms are demonstrated: conversational integration for exploratory tasks and agentic workflow integration for systematic automation. End-to-end demonstrations on a residential energy model show an 80% to 90% time reduction for model inspection, modification, and analysis tasks. The framework augments rather than replaces professional judgment, with AI handling tool orchestration while practitioners retain decision-making authority. The approach establishes a foundational infrastructure for advanced capabilities including parametric simulations, BIM-to-BEM translation, and automated model calibration.