Large Language Models (LLMs) are increasingly used in AI-based software engineering, but their limitations in complex task execution and multi-tool coordination have driven growing interest in the Model Context Protocol (MCP). Existing research has mainly focused on MCP's technical design, with limited empirical evidence on how it is adopted and used in enterprise practice, particularly with regard to deployment challenges, operational risks, and practitioner expectations. To address this gap, we conducted semi-structured interviews with 20 practitioners from eight companies in the Internet and financial sectors. The findings show that MCP is valued for supporting cross-system collaboration, task decoupling, and knowledge reuse in LLM-based workflows, but its adoption remains constrained by ecosystem fragmentation, cross-component coordination difficulties, and unresolved problems in distributed state management and fault diagnosis. Participants also expressed strong demand for better standardization, lower adoption barriers through low-code or plugin-based approaches, and more systematic operational support. These results provide early empirical evidence on enterprise MCP practice and offer practical implications for improving MCP's standardization, usability, and deployment readiness in real-world software engineering environments.

翻译：大语言模型（LLM）在基于AI的软件工程中应用日益广泛，但它们在复杂任务执行和多工具协调方面的局限性，推动了对模型上下文协议（MCP）日益增长的兴趣。现有研究主要集中在MCP的技术设计上，关于其如何在企业实践中被采用和使用的实证证据有限，特别是在部署挑战、运营风险和从业者期望方面。为填补这一空白，我们对来自互联网和金融行业八家企业的20名从业者进行了半结构化访谈。研究结果表明，MCP在支持基于LLM的工作流中的跨系统协作、任务解耦和知识复用方面受到重视，但其采用仍受到生态系统碎片化、跨组件协调困难以及分布式状态管理和故障诊断中未解决问题的制约。参与者还表达了对更好标准化、通过低代码或基于插件的方法降低采用障碍、以及更系统化运营支持的强烈需求。这些结果为企业的MCP实践提供了早期实证依据，并为在现实软件工程环境中提高MCP的标准化、可用性和部署就绪度提供了实际启示。