超越能力与技能的正式语义：制造业中的模型上下文协议 (Beyond Formal Semantics for Capabilities and Skills: Model Context Protocol in Manufacturing)

from arxiv, \c{opyright} 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Explicit modeling of capabilities and skills -- whether based on ontologies, Asset Administration Shells, or other technologies -- requires considerable manual effort and often results in representations that are not easily accessible to Large Language Models (LLMs). In this work-in-progress paper, we present an alternative approach based on the recently introduced Model Context Protocol (MCP). MCP allows systems to expose functionality through a standardized interface that is directly consumable by LLM-based agents. We conduct a prototypical evaluation on a laboratory-scale manufacturing system, where resource functions are made available via MCP. A general-purpose LLM is then tasked with planning and executing a multi-step process, including constraint handling and the invocation of resource functions via MCP. The results indicate that such an approach can enable flexible industrial automation without relying on explicit semantic models. This work lays the basis for further exploration of external tool integration in LLM-driven production systems.

翻译：对能力与技能的显式建模——无论是基于本体论、资产管理系统还是其他技术——需要大量人工投入，且往往产生难以被大型语言模型（LLM）直接访问的表示形式。在这篇进展中的论文中，我们提出了一种基于近期引入的模型上下文协议（MCP）的替代方案。MCP允许系统通过标准化接口暴露功能，该接口可直接由基于LLM的智能体调用。我们在一个实验室规模的制造系统上进行了原型评估，其中资源功能通过MCP提供。随后，一个通用LLM被赋予规划并执行多步骤流程的任务，包括约束处理以及通过MCP调用资源功能。结果表明，这种方法能够在不依赖显式语义模型的情况下实现灵活的工业自动化。本研究为在LLM驱动的生产系统中进一步探索外部工具集成奠定了基础。