AI Tool Discovery at Scale: All You Need is DNS

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[Submitted on 19 Apr 2026]

Title:AI Tool Discovery at Scale: All You Need is DNS

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Abstract: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.

Comments: keywords: AI tool discovery, ToolDNS, Agent, DNS

Subjects:

Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Networking and Internet Architecture (cs.NI)

Cite as: arXiv:2607.18242 [cs.AI]

(or arXiv:2607.18242v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.18242

arXiv-issued DOI via DataCite

Submission history

From: Yulin Shao [view email] [v1] Sun, 19 Apr 2026 04:31:19 UTC (633 KB)

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