A Modular Reference Architecture for MCP-Servers Enabling Agentic BIM Interaction

• URL: http://arxiv.org/abs/2601.00809v1
• Date: Sun, 21 Dec 2025 23:12:26 GMT
• Title: A Modular Reference Architecture for MCP-Servers Enabling Agentic BIM Interaction
• Authors: Tobias Heimig-Elschner, Changyu Du, Anna Scheuvens, André Borrmann, Jakob Beetz,
• Abstract summary: 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.
• Score: 0.5219568203653523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agentic workflows driven by large language models (LLMs) are increasingly applied to Building Information Modelling (BIM), enabling natural-language retrieval, modification and generation of IFC models. Recent work has begun adopting the emerging Model Context Protocol (MCP) as a uniform tool-calling interface for LLMs, simplifying the agent side of BIM interaction. While MCP standardises how LLMs invoke tools, current BIM-side implementations are still authoring tool-specific and ad hoc, limiting reuse, evaluation, and workflow portability across environments. This paper addresses this gap by introducing a modular reference architecture for MCP servers that enables API-agnostic, isolated and reproducible agentic BIM interactions. From a systematic analysis of recurring capabilities in recent literature, we derive a core set of requirements. These inform a microservice architecture centred on an explicit adapter contract that decouples the MCP interface from specific BIM-APIs. A prototype implementation using IfcOpenShell demonstrates feasibility across common modification and generation tasks. Evaluation across representative scenarios shows that the architecture enables reliable workflows, reduces coupling, and provides a reusable foundation for systematic research.

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