Agentic Resource Discovery, or ARD, is an open specification for telling AI agents what a site offers, in one machine-readable file. Instead of inferring from pages whether a site has an MCP server, an agent interface, or an API, the site publishes a single index that names each resource and where to reach it. The specification appeared in 2026, is licensed under Apache 2.0, and builds on the AI Catalog data model maintained by a working group under the Linux Foundation.
A site advertises its agentic resources by serving a static JSON manifest at /.well-known/ai-catalog.json. The manifest is a small envelope with a specVersion (turva.dev publishes 1.0), a host block that names the operator, and an entries array. Each entry describes one resource with a stable identifier, a display name, a type, a url, and a short description. A resource can be an MCP server, an A2A agent, an API, or a skill set. A registry can crawl published catalogs and answer a capability query by pointing an agent at the right resource.
ARD is a discovery layer, not a transport. It helps an agent find the right resource, which the agent then calls through that resource's own protocol, whether MCP, A2A, or a plain API. Discovery comes first and invocation second. The catalog does not replace the manifests it points to, it indexes them, so a site keeps its server card, its agent card, and its OpenAPI description, and adds one file that ties them together.
An ai-catalog.json is not a ranking trick and it is not a content map. llms.txt tells an agent where a site's content lives. An ai-catalog tells an agent which agentic resources the site exposes and how to reach them. The two are complementary, and neither is about search ranking. Google confirmed in 2026 that llms.txt does not affect its search results, which is the same point agent-readiness has always made. These files are for agents that read and act.
Adoption is early. In a public census in June 2026, none of the companies named as contributors to the specification yet served a discoverable ai-catalog.json, so publishing one now is a forward move rather than table stakes. The value is the same as every other discovery surface. A capability an agent cannot find is a capability that does not exist for that agent, and one predictable file turns a set of separate manifests into a single answer.
turva.dev serves an ai-catalog.json at /.well-known/ai-catalog.json that indexes its MCP server, its A2A agent, its API, and its agent skills, each of which already resolves on its own. For an audit of a site's discovery surface, contact info@turva.dev.
Originally published at https://turva.dev/guides/agentic-resource-discovery