标题： HGMF：一种分层高斯混合框架，用于模型上下文协议内的可扩展工具调用 显示英文标题

标题： HGMF: A Hierarchical Gaussian Mixture Framework for Scalable Tool Invocation within the Model Context Protocol

摘要： 调用外部工具使大型语言模型（LLMs）能够执行复杂的真实任务，但从大型分层结构的库中选择正确的工具仍然是一个重大挑战。 LLMs 有限的上下文窗口和无关选项的噪声常常导致选择准确性低和计算成本高。 为了解决这个问题，我们提出了分层高斯混合框架（HGMF），这是一种用于可扩展工具调用的概率剪枝方法。 HGMF 首先将用户查询和所有工具描述映射到一个统一的语义空间。 该框架然后分为两个阶段：它使用高斯混合模型（GMM）对服务器进行聚类，并根据查询的可能性进行过滤。 随后，它对所选服务器相关的工具应用相同的基于 GMM 的聚类和过滤。 这一分层过程生成一个紧凑且高相关性的候选集，简化了 LLM 的最终选择任务。 在公共数据集上的实验表明，HGMF 显著提高了工具选择的准确性，同时减少了推理延迟，证实了该框架在大规模工具库中的可扩展性和有效性。 显示英文摘要

摘要： Invoking external tools enables Large Language Models (LLMs) to perform complex, real-world tasks, yet selecting the correct tool from large, hierarchically-structured libraries remains a significant challenge. The limited context windows of LLMs and noise from irrelevant options often lead to low selection accuracy and high computational costs. To address this, we propose the Hierarchical Gaussian Mixture Framework (HGMF), a probabilistic pruning method for scalable tool invocation. HGMF first maps the user query and all tool descriptions into a unified semantic space. The framework then operates in two stages: it clusters servers using a Gaussian Mixture Model (GMM) and filters them based on the query's likelihood. Subsequently, it applies the same GMM-based clustering and filtering to the tools associated with the selected servers. This hierarchical process produces a compact, high-relevance candidate set, simplifying the final selection task for the LLM. Experiments on a public dataset show that HGMF significantly improves tool selection accuracy while reducing inference latency, confirming the framework's scalability and effectiveness for large-scale tool libraries.