Agentic Resource Discovery: Let agents search

If you build with agents today, you probably know three protocols. MCP gives agents a standard way to call tools. Skills give agents a way of consuming instructions. A2A gives agents a way to call other agents. All three assume the user already knows which tool, instruction, or agent they need. The user is still responsible for discovering, integrating, and maintaining those capabilities.

The Agentic Resource Discovery (ARD) specification is the discovery layer that sits in front of them. It is a draft, open specification developed by contributors from Microsoft, Google, GoDaddy, Hugging Face, and others, with broad participation across the industry. 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.

The specification defines two things:

• A static manifest format called ai-catalog.json lets publishers host their capabilities at a well-known URL.
• A dynamic registry API at POST /search provides live, ranked discovery.