Realizing a Model Context Protocol (MCP) Server for the Graphical Language Server Platform (GLSP)

Abstract

Model-Driven Engineering (MDE) offers strong abstractions but remains hindered in practice by complex tooling and steep learning curves. Recent advances in Large Language Models (LLMs) promise intuitive, natural-language interaction with modeling environments; however, existing approaches—such as direct API interaction, DSL generation, or manipulation of serialized models—exhibit limited reliability, scalability, and semantic precision. This paper presents a structured integration of LLMs into graphical modeling environments via the Model Context Protocol (MCP). We design and implement a reusable MCP server for the Graphical Language Server Platform (GLSP), enabling LLM agents to interact with models through semantically grounded, tool-based operations rather than direct model generation. We contribute (i) a reference architecture for MCP-based integration into GLSP derived from a systematic literature review of MCP implementations, (ii) design principles for mapping modeling concepts to MCP primitives and abstraction levels, and (iii) an empirical evaluation assessing the impact of MCP-based interaction on downstream modeling tasks. Our results show that MCP-based interactions significantly improve both information retrieval (perfect precision/recall across tasks) and model manipulation (reducing syntactic, semantic, and pragmatic errors), while also improving scalability and efficiency compared to serialization-based approaches. The MCP server enables robust modification of existing models, a key limitation of prior approaches. These findings demonstrate that structured, tool-mediated interaction is a crucial enabler for reliable AI-assisted modeling. The presented MCP–GLSP Server constitutes a reusable artifact applicable across GLSP-based tools, providing a practical foundation to advance intelligent modeling assistance.