MCP4IFC: IFC-Based Building Design Using Large Language Models

• URL: http://arxiv.org/abs/2511.05533v1
• Date: Wed, 29 Oct 2025 09:14:14 GMT
• Title: MCP4IFC: IFC-Based Building Design Using Large Language Models
• Authors: Bharathi Kannan Nithyanantham, Tobias Sesterhenn, Ashwin Nedungadi, Sergio Peral Garijo, Janis Zenkner, Christian Bartelt, Stefan Lüdtke,
• Abstract summary: MCP4IFC is a comprehensive open-source framework that enables Large Language Models (LLMs) to manipulate Industry Foundation Classes (IFC) data.<n>Our framework is released as open-source to encourage research in BIM-driven design and provide a foundation for AI-assisted modeling.
• Score: 10.715011902262617
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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/.

Related papers

• Monadic Context Engineering [59.95390010097654]
  This paper introduces Monadic Context Engineering (MCE) to provide a formal foundation for agent design.<n>We demonstrate how Monads enable robust composition, how Applicatives provide a principled structure for parallel execution, and crucially, how Monad Transformers allow for the systematic composition of these capabilities.<n>This layered approach enables developers to construct complex, resilient, and efficient AI agents from simple, independently verifiable components.
  arXiv  Detail & Related papers  (2025-12-27T01:52:06Z)
• HELP: Hierarchical Embodied Language Planner for Household Tasks [75.38606213726906]
  Embodied agents tasked with complex scenarios rely heavily on robust planning capabilities.<n>Large language models equipped with extensive linguistic knowledge can play this role.<n>We propose a Hierarchical Embodied Language Planner, called HELP, consisting of a set of LLM-based agents.
  arXiv  Detail & Related papers  (2025-12-25T15:54:08Z)
• A Modular Reference Architecture for MCP-Servers Enabling Agentic BIM Interaction [0.5219568203653523]
  Agentic driven by large language models (LLMs) are increasingly applied to Building Information Modelling.<n>Recent work has begun adopting the emerging Model Context Protocol (MCP) as a uniform tool-calling interface for LLMs.<n>Current BIM-side implementations are still authoring tool-specific and ad hoc, limiting reuse, evaluation, and workflow portability across environments.<n>This paper introduces a modular reference architecture for MCP servers that enables API-agnostic, isolated and reproducible agentic BIM interactions.
  arXiv  Detail & Related papers  (2025-12-21T23:12:26Z)
• Large Language Models are Good Relational Learners [55.40941576497973]
  We introduce Rel-LLM, a novel architecture that utilizes a graph neural network (GNN)- based encoder to generate structured relational prompts for large language models (LLMs)<n>Unlike traditional text-based serialization approaches, our method preserves the inherent relational structure of databases while enabling LLMs to process and reason over complex entity relationships.
  arXiv  Detail & Related papers  (2025-06-06T04:07:55Z)
• GUI Agents with Foundation Models: A Comprehensive Survey [91.97447457550703]
  This survey consolidates recent research on (M)LLM-based GUI agents.<n>We identify key challenges and propose future research directions.<n>We hope this survey will inspire further advancements in the field of (M)LLM-based GUI agents.
  arXiv  Detail & Related papers  (2024-11-07T17:28:10Z)
• Control Industrial Automation System with Large Language Model Agents [2.2369578015657954]
  This paper introduces a framework for integrating large language models with industrial automation systems.<n>At the core of the framework are an agent system designed for industrial tasks, a structured prompting method, and an event-driven information modeling mechanism.<n>Our contribution includes a formal system design, proof-of-concept implementation, and a method for generating task-specific datasets.
  arXiv  Detail & Related papers  (2024-09-26T16:19:37Z)
• The Compressor-Retriever Architecture for Language Model OS [20.56093501980724]
  This paper explores the concept of using a language model as the core component of an operating system (OS) A key challenge in realizing such an LM OS is managing the life-long context and ensuring statefulness across sessions. We introduce compressor-retriever, a model-agnostic architecture designed for life-long context management.
  arXiv  Detail & Related papers  (2024-09-02T23:28:15Z)
• Text2BIM: Generating Building Models Using a Large Language Model-based Multi-Agent Framework [0.3749861135832073]
  The Text2 BIM framework generates 3D building models from natural language instructions.<n>A rule-based model checker is introduced into the agentic workflow to guide the LLM agents in resolving issues.<n>The framework can effectively generate high-quality, structurally rational building models.
  arXiv  Detail & Related papers  (2024-08-15T09:48:45Z)
• CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets [75.64181719386497]
  We present CRAFT, a tool creation and retrieval framework for large language models (LLMs) It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks. Our method is designed to be flexible and offers a plug-and-play approach to adapt off-the-shelf LLMs to unseen domains and modalities, without any finetuning.
  arXiv  Detail & Related papers  (2023-09-29T17:40:26Z)
• ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models [74.64651681052628]
  We introduce ModelScope-Agent, a customizable agent framework for real-world applications based on open-source LLMs as controllers. It provides a user-friendly system library, with customizable engine design to support model training on multiple open-source LLMs. A comprehensive framework has been proposed spanning over tool-use data collection, tool retrieval, tool registration, memory control, customized model training, and evaluation.
  arXiv  Detail & Related papers  (2023-09-02T16:50:30Z)
• CodeTF: One-stop Transformer Library for State-of-the-art Code LLM [72.1638273937025]
  We present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. Our library supports a collection of pretrained Code LLM models and popular code benchmarks. We hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering.
  arXiv  Detail & Related papers  (2023-05-31T05:24:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.