Understanding How Enterprises Adopt the Model Context Protocol for LLM-Driven Software Engineering
2026-06-08 • Software Engineering
Software Engineering
AI summaryⓘ
The authors studied how companies use the Model Context Protocol (MCP) to help large language models work better in software engineering tasks. They interviewed 20 practitioners and found that while MCP helps with teamwork across different systems and reusing knowledge, it's hard to adopt because of technical challenges and lack of standards. The practitioners want easier ways to use MCP, like simpler tools and better support for running it smoothly. The authors provide early real-world insights that can help improve MCP for practical use.
Large Language ModelsModel Context ProtocolSoftware EngineeringCross-system CollaborationTask DecouplingKnowledge ReuseEcosystem FragmentationDistributed State ManagementFault DiagnosisStandardization
Authors
Kehui Chen, Yicheng Sun, Jacky Keung, Zhenyu Mao, Xiaoxue Ma
Abstract
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.