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. The shift is from manually installed, static catalogs to intent-based search that lets an agent find the right capability dynamically, and reach a growing ecosystem of MCP tools, A2A agents, and other services without pre-configuring each one.

The Hugging Face Discover Tool is our reference implementation of ARD. It provides search access to thousands of Skills, ML applications, and MCP Servers — on Hugging Face and across other ARD discovery services.