Agentic Resource Discovery: Let agents search
Hugging Face outlined updates on Agentic Resource Discovery: Let agents search: agentic Resource Discovery: Let agents search
It defines how agents and tools are cataloged, indexed, and searched across federated registries, so an agent can find capabilities at runtime instead of needing them pre-installed. It is not a product or a marketplace. It is a shared standard that any company can implement independently, and that any agent or tool can participate in. In this post, we’ll explore the specification, how Hugging Face has implemented it, and how you can start building on ARD. The discovery problem The current model for agent capabilities is install-first, use-later. A developer hardcodes an MCP server URL into a config file. A user connects a service to their AI app via a plugin and reuses it. This works for the handful of tools an agent uses every day, but it doesn’t scale to thousands of ad-hoc surfaces. The fallback is to dump every available tool description into the LLM’s context window and let the model pick. This is limited by the context budget. There are search-based strategies here too, but the descriptions are often too thin to disambiguate well. ARD moves selection outside the LLM. A registry indexes capabilities with richer signals such as publisher identity, representative queries, compliance attestations, and tags. It exposes a REST endpoint. The client searches in natural language, and the model invokes whatever the search returns.
