CityDPC-MCP: Enabling 3D city model processing via Model Context Protocol
Abstract
3D city models in CityGML and CityJSON provide an important data foundation for urban analysis, yet they remain difficult to use in practice. Datasets are often large and heterogeneous, attributes may be missing, and many workflows require specialized domain knowledge. Large Language Models (LLMs) can lower this barrier by serving as natural language interfaces, provided that they interact with the underlying data structures reliably and consistently. This paper introduces CityDPC-MCP, a Model Context Protocol (MCP) server that wraps predefined functions for loading, querying, analyzing, and modifying 3D city models within a stateful dataset. The server is evaluated against two configurations available to users today: an LLM operating directly on the provided file and an LLM with code execution in a sandbox. Across five test cases, three models, and 100 repetitions each, the MCP setup achieved higher success rates while keeping the dataset out of the model context, which decouples token consumption from file size and format. An additional exploratory experiment on dataset generation illustrates possible agentic workflows and their current limitations.
Authors 2
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Affiliation as printed
Institute of Energy Efficiency and Sustainable Building (E3D), RWTH Aachen University, Germany; 2E3D Ingenieurgesellschaft mbH, Germany
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Affiliation as printed
Institute of Energy Efficiency and Sustainable Building (E3D), RWTH Aachen University, Germany