大规模人工智能工具发现：你所需要的只是DNS
AI Tool Discovery at Scale: All You Need is DNS
- The University of Hong Kong（香港大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对自主人工智能代理时代工具发现难题，提出ToolDNS框架，通过嵌入意图和信任将语义搜索转为轻量级名称解析，引入三项增强。经大规模基准测试验证，其在减少搜索空间和降低延迟方面效果显著，证明可通过利用现有基础设施实现可扩展的AI互操作性。
AI中文摘要：
自主人工智能代理的时代需要一种能在数百万工具中导航的发现机制，现有解决方案因O(N)复杂度和集中治理而受限。我们提出ToolDNS，将语义工具发现改造到互联网最具弹性的基础架构DNS上。通过嵌入功能意图和组织信任，将昂贵的语义搜索转变为轻量级的O(log N)名称解析。引入三项协议兼容增强实现去中心化治理和语义修剪。构建并发布大规模异构基准测试，ToolDNS在匹配最先进检索精度的同时，将每个查询的搜索空间减少95.26%，且UDP原生设计大幅降低发现延迟。
英文摘要：
The coming era of autonomous AI agents demands a discovery mechanism capable of navigating millions of tools, yet existing solutions buckle under O(N) complexity and centralized governance. Instead of building another fragile overlay, we propose ToolDNS, a radical framework that retrofits semantic tool discovery onto the Internet's most resilient substrate: the Domain Name System (DNS). By embedding functional intent and organizational trust into a hierarchical namespace, ToolDNS transforms an expensive semantic search into a series of lightweight, O(log N) name resolutions. We introduce three protocol-compliant enhancements to enable decentralized governance and semantic pruning: partially unfolded names, EDNS0 intent payloads, and logical subdomains. To rigorously evaluate this approach across the fragmented tooling landscape, we construct and release a large-scale heterogeneous benchmark comprising 33,688 real-world tools spanning MCP, A2A, RESTful, and Skill protocols. On this dataset, ToolDNS slashes the per-query search space by 95.26% while matching state-of-the-art retrieval accuracy. Furthermore, its UDP-native design reduces discovery latency by orders of magnitude compared to HTTP-based registries. Our work demonstrates that scalable AI interoperability requires not more middleware, but a smarter utilization of the infrastructure already beneath our feet.
