Beyond Formal Semantics for Capabilities and Skills: Model Context Protocol in Manufacturing
On the map: LLM agents and tool use → Tool learning and function calling
Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 7 of its 12 references that are in the map. First posted on arXiv 12 Jun 2025.
Builds on
7 of 12 references.
References · 7
- 2025Large Language Models for Multi-Robot Systems: A Surveycited by 27
- 2025LLM-Powered AI Agent Systems and Their Applications in Industrycited by 22
- 2024Autonomous Industrial Control using an Agentic Framework with Large Language Modelscited by 19
- 2024Control Industrial Automation System with Large Language Model Agentscited by 11
- 2022A Reference Model for Common Understanding of Capabilities and Skills in Manufacturingcited by 9
- 2024Toward a Method to Generate Capability Ontologies from Natural Language Descriptionscited by 4
- 2025Capability-Driven Skill Generation with LLMs: A RAG-Based Approach for Reusing Existing Libraries and Interfacescited by 2
Built on by
Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2025Benchmark for Planning and Control with Large Language Model Agents: Blocksworld with Model Context Protocolcited by 3
- 2026Understanding How Enterprises Adopt the Model Context Protocol for LLM-Driven Software Engineeringcited by 0
- 2026Towards an Intention Abstraction Layer for Autonomous Industrial Systemscited by 0
- 2026LLM-Driven Multi-Agent Control for Skill-Based Smart Manufacturingcited by 0
- 2026LLM-Driven Multi-Agent Control for Skill-Based Smart Manufacturing1 Oct 2026
- 2026Towards an Intention Abstraction Layer for Autonomous Industrial Systems16 Jul 2026
- 2026Understanding How Enterprises Adopt the Model Context Protocol for LLM-Driven Software Engineering8 Jun 2026
- 2025Benchmark for Planning and Control with Large Language Model Agents: Blocksworld with Model Context Protocol3 Dec 2025
Similar, not cited
Close in wording, no citation either way
- 2024Incorporating Large Language Models into Production Systems for Enhanced Task Automation and Flexibility
- 2026The Semantic Training Gap: Ontology-Grounded Tool Architectures for Industrial AI Agent Systems
- 2025Help or Hurdle? Rethinking Model Context Protocol-Augmented Large Language Models
- 2025Network and Systems Performance Characterization of MCP-Enabled LLM Agents
- 2026MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers
- 2025MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers
- 2026Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptions
- 2026Enhancing Model Context Protocol (MCP) with Context-Aware Server Collaboration