# Understanding How Enterprises Adopt the Model Context Protocol for LLM-Driven Software Engineering

arXiv: https://arxiv.org/abs/2606.09182 · HTML: https://mlsanity.com/paper/2606.09182/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use.
- Builds on 20 of its 44 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Placed in

- LLM agents and tool use / [Tool learning and function calling](https://mlsanity.com/agents/tool-learning-and-function-calling/)

## Builds on (most cited first, 20 of 20)

- [Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions](https://mlsanity.com/paper/2503.23278/) (2025)
- [AutoAgents: A Framework for Automatic Agent Generation](https://mlsanity.com/paper/2309.17288/) (2023)
- [ToolACE: Winning the Points of LLM Function Calling](https://mlsanity.com/paper/2409.00920/) (2024)
- [Large Language Model-Brained GUI Agents: A Survey](https://mlsanity.com/paper/2411.18279/) (2024)
- [MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers](https://mlsanity.com/paper/2508.20453/) (2025)
- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
- [A Survey of AI Agent Protocols](https://mlsanity.com/paper/2504.16736/) (2025)
- [Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents](https://mlsanity.com/paper/2411.09523/) (2024)
- [Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers](https://mlsanity.com/paper/2506.13538/) (2025)
- [MCPSecBench: A Systematic Security Benchmark and Playground for Testing Model Context Protocols](https://mlsanity.com/paper/2508.13220/) (2025)
- [LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?](https://mlsanity.com/paper/2508.01780/) (2025)
- [Enterprise-Grade Security for the Model Context Protocol (MCP): Frameworks and Mitigation Strategies](https://mlsanity.com/paper/2504.08623/) (2025)
- [Securing the Model Context Protocol (MCP): Risks, Controls, and Governance](https://mlsanity.com/paper/2511.20920/) (2025)
- [When MCP Servers Attack: Taxonomy, Feasibility, and Mitigation](https://mlsanity.com/paper/2509.24272/) (2025)
- [Semantic Attacks on Tool-Augmented LLMs: Securing the Model Context Protocol Against Descriptor-Level Manipulation](https://mlsanity.com/paper/2512.06556/) (2025)
- [Evaluation Report on MCP Servers](https://mlsanity.com/paper/2504.11094/) (2025)
- [Practitioners' Expectations on Code Completion](https://mlsanity.com/paper/2301.03846/) (2023)
- [Beyond Formal Semantics for Capabilities and Skills: Model Context Protocol in Manufacturing](https://mlsanity.com/paper/2506.11180/) (2025)
- [Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol](https://mlsanity.com/paper/2506.13068/) (2025)
- [Can LLM Infer Risk Information From MCP Server System Logs?](https://mlsanity.com/paper/2511.05867/) (2025)

## Abstract (arXiv)

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.