DeltaMCP智能增量更新MCP服务器，省去重写全部代码的麻烦。
DeltaMCP: Incremental Regeneration via Spec-Aware Transformation for MCP servers
- 基于OpenAPI规范变化，仅更新受影响的工具模块。
- 相比全量生成，开发成本降低40%以上，版本一致性提升。
- 适合需要频繁维护企业级API的LLM系统开发者。
大语言模型（LLMs）与模型上下文协议（MCP）的快速发展，推动了智能代理通过确定性、结构化方式调用API。尽管已有系统如AutoMCP尝试自动化生成MCP服务器，但无法解决企业级API演进与对应MCP工具集同步的持续挑战。本文提出DeltaMCP，一种面向规范的增量再生工具，可针对服务新版本的OpenAPI规范，仅更新受影响的MCP服务器组件。以Azure REST API规范为数据集，实验对比了全量生成基线方法在生成质量与系统性能上的表现。结果表明，DeltaMCP显著降低开发负担，提升可维护性与版本一致性。本研究为寻求高保真、实时更新的LLM系统MCP基础设施的企业提供了可扩展解决方案。
原文摘要 · Abstract (English)
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.
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