The rapid development of LLMs coupled with the introduction of Model Context Protocol (MCP) has revolutionized how intelligent agents interact with APIs through deterministic and structured methods \cite{ModelContextProtocolIntro2025}. While some existing systems like AutoMCP attempt to automate a previously completely manual process of generating MCP servers, they fail to address the recurring challenge of maintaining synchronization between evolving enterprise-level APIs and their corresponding MCP toolset implementation \cite{mastouri2025makingrestapisagentready}. This paper introduces DeltaMCP, a specification-aware, incremental regeneration tool for enterprise-grade MCP servers. DeltaMCP enables developers to only update the affected tooling of MCP servers, given a new release of it's corresponding service's OpenAPI specification. Using Azure REST API specifications as the evaluation dataset, DeltaMCP is benchmarked against baseline full generation methods on generation quality and system performance. The results demonstrate the reduction in developer overhead through DeltaMCP whilst improving maintainability and version consistency. This research offers a scalable approach for enterprises seeking to maintain high-fidelity, up-to-date MCP server infrastructures for LLM-based systems.

翻译：大型语言模型的快速发展，加上模型上下文协议（MCP）的引入，通过确定性和结构化的方法彻底改变了智能代理与API交互的方式\cite{ModelContextProtocolIntro2025}。尽管现有系统（如AutoMCP）尝试将此前完全手动的MCP服务器生成流程自动化，但它们未能解决企业级API与其对应MCP工具集实现之间持续同步的反复挑战\cite{mastouri2025makingrestapisagentready}。本文提出了DeltaMCP，一种面向企业级MCP服务器的规范感知增量再生工具。当对应服务的OpenAPI规范发布新版本时，DeltaMCP使开发者能够仅更新MCP服务器中受影响的工具组件。以Azure REST API规范作为评估数据集，DeltaMCP在生成质量和系统性能方面与基线完整生成方法进行了基准测试。结果表明，DeltaMCP在降低开发者负担的同时，提升了可维护性与版本一致性。这项研究为致力于为基于LLM的系统维持高保真、最新MCP服务器基础设施的企业提供了一种可扩展的方案。