Large Language Models (LLMs) increasingly rely on emerging protocols such as the Model Context Protocol (MCP) to invoke external tools and services. However, current tool routing mechanisms remain fragile because they only consider functional matching between users' queries and tools. In practice, user intent expressed through queries can be vague or underspecified, and the actual Quality of Experience (QoE) also depends on external factors such as link latency and server availability that are not captured by semantics alone. To address this challenge, we propose JAUNT, a framework for Joint Alignment of User intent and Network state in QoE-centric Tool routing. JAUNT introduces a dual-view alignment strategy that interprets user intent while employing LLM agents to construct network profiles, mapping numerical performance indicators into the semantic space to guide routing. We further design a benchmark that integrates diverse user request patterns with heterogeneous network states, enabling systematic evaluation of QoE outcomes. Experimental results show that JAUNT significantly improves QoE compared with several baselines, demonstrating the importance of aligning both intent and network state for scalable LLM service orchestration.

翻译：大语言模型（LLMs）日益依赖新兴协议（如模型上下文协议 MCP）来调用外部工具与服务。然而，当前的工具路由机制仍显脆弱，因其仅考虑用户查询与工具之间的功能匹配。实际上，通过查询表达的用户意图可能模糊或未充分明确，而实际体验质量（QoE）还取决于外部因素（如链路延迟和服务器可用性），这些因素无法仅通过语义信息捕捉。为应对这一挑战，我们提出了JAUNT，一个面向体验质量（QoE）的工具路由中用户意图与网络状态联合对齐的框架。JAUNT引入了一种双视图对齐策略，在解析用户意图的同时，利用LLM智能体构建网络画像，将数值性能指标映射到语义空间以指导路由决策。我们进一步设计了一个基准测试，将多样化的用户请求模式与异构网络状态相结合，从而实现对QoE结果的系统性评估。实验结果表明，与多种基线方法相比，JAUNT显著提升了QoE，证明了在可扩展的LLM服务编排中联合对齐意图与网络状态的重要性。