Bringing generative AI into the architecture, engineering and construction (AEC) field requires systems that can translate natural language instructions into actions on standardized data models. We present MCP4IFC, a comprehensive open-source framework that enables Large Language Models (LLMs) to directly manipulate Industry Foundation Classes (IFC) data through the Model Context Protocol (MCP). The framework provides a set of BIM tools, including scene querying tools for information retrieval, predefined functions for creating and modifying common building elements, and a dynamic code-generation system that combines in-context learning with retrieval-augmented generation (RAG) to handle tasks beyond the predefined toolset. Experiments demonstrate that an LLM using our framework can successfully perform complex tasks, from building a simple house to querying and editing existing IFC data. Our framework is released as open-source to encourage research in LLM-driven BIM design and provide a foundation for AI-assisted modeling workflows. Our code is available at https://show2instruct.github.io/mcp4ifc/.
@article{arxiv.2511.05533,
title = {MCP4IFC: IFC-Based Building Design Using Large Language Models},
author = {Bharathi Kannan Nithyanantham and Tobias Sesterhenn and Ashwin Nedungadi and Sergio Peral Garijo and Janis Zenkner and Christian Bartelt and Stefan Lüdtke},
journal= {arXiv preprint arXiv:2511.05533},
year = {2025}
}