首次系统梳理MCP服务器运行时故障，揭示11类27种常见问题。
A Taxonomy of Runtime Faults in Model Context Protocol Servers
- 通过分析837个GitHub故障帖，构建自下而上的故障分类体系。
- 开发者调查显示平均遭遇20类故障，所有类别均被实际观察到。
- 适合关注AI系统可靠性的开发与运维人员参考。
MCP（模型上下文协议）通过标准化协议使大语言模型能够与外部工具和数据源交互。其在工具增强型人工智能工作流中的快速应用带来了新的可靠性挑战，例如运行时未执行的配置参数导致意外默认行为，而此类运行时故障特征尚未得到实证研究。本文首次提出基于实证的MCP服务器运行时故障分类体系。我们手动分析了473个活跃维护的MCP服务器GitHub仓库中的837个特定故障线程，并采用自下而上的开放式编码方法构建分类体系。该分类包含11个顶层类别和27个子类别（共73个叶级故障类型），涵盖协议交互、工具调用、模式校验、状态管理、模型提供方集成、安全验证以及超时或主动取消操作等常见失败场景。为评估分类体系的外部有效性，我们对55名MCP服务器开发者进行了调查。受访者报告平均遭遇20个子类别故障，且无一类别未被观察到。结果表明，该分类体系反映了广泛存在的MCP系统运行时故障，未来将有助于人工智能软件的维护与演进。
原文摘要 · Abstract (English)
MCP (Model Context Protocol) enables LLMs (Large Language Models) to interact with external tools and data sources via a standardized protocol. Its rapid adoption in tool-augmented Artificial Intelligence (AI) workflows has introduced new reliability challenges, such as configuration parameters that are accepted but not enforced at runtime, leading to unintended default behavior, whose runtime fault characteristics remain empirically unexamined. We present the first empirical taxonomy of runtime faults in MCP servers. We manually analyzed 837 MCP-specific runtime fault threads from 473 actively maintained MCP server GitHub repositories and derived a taxonomy using a bottom-up open coding procedure. The taxonomy comprises 11 top-level categories and 27 subcategories (73 leaf fault types), covering recurrent failures across protocol interactions, tool invocations, schema enforcement, state management, model-provider integration, security validation, and timeouts or explicit cancellations of in-progress operations. To assess the taxonomy's external validity, we surveyed 55 MCP server developers. Respondents reported experiencing an average of 20 of the 27 fault subcategories, and no category remained unobserved. These results indicate that the taxonomy reflects widely observed runtime failures in MCP-based systems and shall assist AI software maintenance and evolution in the future.
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