Agentic Resource Discovery: Let agents search Microsoft, Google, GoDaddy, Hugging Face, and other contributors have released the Agentic Resource Discovery (ARD) specification, an open standard for dynamically discovering AI agent tools and capabilities at runtime. The specification defines a manifest format and registry API to replace static, pre-installed tool configurations with intent-based search. Hugging Face has launched a reference implementation on its Hub, enabling agents to search thousands of skills, MCP servers, and applications across federated registries. Agentic Resource Discovery: Let agents search for tools, skills, and other agents. Update on GitHub https://github.com/huggingface/blog/blob/main/agentic-resource-discovery-launch.md 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. 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 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. ARD on the Hugging Face Hub The Hugging Face Discover Tool https://github.com/huggingface/hf-discover 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. It works by combining the Hub's existing semantic search over Spaces, alongside our Agent Skills, and serving the results as ARD catalog entries. The Hub already hosts a catalog of Spaces running Gradio apps, MCP servers, and demos. Its semantic search supports an agents=true flag that returns Spaces ranked by agent-oriented metadata, and Discover translates that search into the ARD specification. The adapter applies two filters. First, the response includes only Spaces whose runtime stage is RUNNING . Second, the response media type is driven by the request. Three media types are supported: application/ai-skill : the default. A generated SKILL.md wrapping the Space's agents.md . application/mcp-server+json : an MCP server catalog entry for Spaces tagged mcp-server . application/vnd.huggingface.space+json : raw Space metadata for clients that want to handle it themselves. The skill type involves an additional transformation. Many Spaces ship an agents.md file describing how an agent should interact with them. Discover reads that file and wraps it with the frontmatter a skill consumer expects: name , description , and source metadata covering the Space ID, Hub URL, app URL, and original agents.md URL. The result is a skill any skill-aware client can install or load through its normal skill flow. For MCP-tagged Spaces, the adapter generates a catalog entry pointing at the Space's Gradio MCP endpoint over HTTP transport. The URL uses the Space's runtime domain when the Hub provides one, otherwise the standard .hf.space slug convention. Using it discover is built into the Hugging Face CLI https://github.com/huggingface/huggingface hub hf . To get started and give you or your agent access: Install the Hugging Face CLI tool: uv tool install huggingface hub Search for resources to train a model hf discover search "Fine tune a language model" Find MCP Servers to generate an image hf discover search "Generate an image" --json --kind mcp Search other registries hf discover search "Purchase aeroplane tickets" --registry-url