Human-Computer Interaction and Human-Building Interaction

MCP-Driven Parametric Modeling: Integrating LLM Agents into Architectural and Landscape Design Workflows

Xun Liu, Runjia Tian

Published 2025

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📄 Paper
We present a novel integration of the Model Context Protocol (MCP) with Grasshopper, enabling Large Language Models to directly interact with parametric modeling workflows for architectural and landscape design. This system allows designers to prompt, iterate, and refine 3D models conversationally through structured symbolic generation, bridging human creative intent and computational form generation. The framework employs a client-server architecture where natural language instructions are parsed into structured commands invoking modular parametric components. Demonstrated in a 9-day DigitalFUTURES workshop with 20 participants across 5 teams, each team developed distinct parametric design lexicons encoding specialized domain knowledge—from generative open spaces to urban functional zoning—that design novices could subsequently operate through natural language interfaces. Beyond accessibility improvements, the protocol-based architecture enables workflow merging through systematic integration of diverse design components into composable ecosystems. We present the system architecture, implementation details, and empirical observations from workshop deployments demonstrating how the approach addresses the theme ofHumanity through expanded creative agency and knowledge democratization.

Abstract

We present a novel integration of the Model Context Protocol (MCP) with Grasshopper, enabling Large Language Models to directly interact with parametric modeling workflows for architectural and landscape design. This system allows designers to prompt, iterate, and refine 3D models conversationally through structured symbolic generation, bridging human creative intent and computational form generation. The framework employs a client-server architecture where natural language instructions are parsed into structured commands invoking modular parametric components. Demonstrated in a 9-day DigitalFUTURES workshop with 20 participants across 5 teams, each team developed distinct parametric design lexicons encoding specialized domain knowledge—from generative open spaces to urban functional zoning—that design novices could subsequently operate through natural language interfaces. Beyond accessibility improvements, the protocol-based architecture enables workflow merging through systematic integration of diverse design components into composable ecosystems. We present the system architecture, implementation details, and empirical observations from workshop deployments demonstrating how the approach addresses the theme ofHumanity through expanded creative agency and knowledge democratization.

Topics

LLMs for AECMultimodal Design AssistanceVision-Language Models for AECXRAI for AECXRAI inclusive design

Cite this paper

@inproceedings{b46f6f01,
title={{MCP}-Driven Parametric Modeling: Integrating {LLM} Agents into Architectural and Landscape Design Workflows},
author={Xun Liu and Runjia Tian},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems Creative AI Track: Humanity},
year={2025},
url={https://openreview.net/forum?id=K7TVfyrQnT}
}