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

Xun Liu, Runjia Tian

Track: Paper

Keywords: Model Context Protocol, Grasshopper, Parametric Modeling, Large Language Models, Architectural Design, Landscape Architecture, AI-assisted Design

TL;DR: An interactive workflow linking LLMs to Grasshopper via Model Context Protocol, enabling conversational control of 3D parametric modeling for architecture and landscape design.

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 of \emph{Humanity} through expanded creative agency and knowledge democratization.

Submission Number: 161