{"slug":"google-s-ard-spec-adds-a-discovery-layer-ahead-of-mcp","citations":[{"url":"https://developers.googleblog.com/announcing-the-agentic-resource-discovery-specification/","committed_hash":"sha256:e941fcea95fb2ef4badd1fcf62deead731a7580a5b0823b9177f436da81a0405","committed_hash_short":"sha256:e941fcea…a81a0405","mime_type":"text/html","committed_at":"2026-09-18T10:00:23.607997+00:00","content_snapshot":"\n\n\n\n \t\n \n \n \n \n Announcing the Agentic Resource Discovery specification\n \n \n - Google Developers Blog\n \n \n \n \n \n\t\n \n \n \n \n \n \n \n \n\n\n \n \n\n \n \n \n\n \n \n \n \n \n\n \n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n \n \n\n \n \n\n \n \n \n\n \n\n \n \n \n \n\n \n\n\t\t\t\t\n \n\n\n
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Announcing the Agentic Resource Discovery specification

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JUNE 17, 2026
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\n Junjie Bu\n \n Senior Staff Software Engineer\n \n \n
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\n Srinivas Krishnan\n \n Distinguished Software Engineer\n \n \n
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An open specification for finding and verifying tools, skills, and agents across the web.

Agents are becoming participants in a much larger ecosystem. They increasingly rely on tools, skills, and other agents distributed across teams, organizations, and platforms.

For this ecosystem to scale, agents need reliable answers to three questions: Where does the right capability live? Which capability should I actually use? And how do I verify it’s safe to connect to?

Today, there is no standard way to answer those questions across organizations.

That’s why we’re announcing Agentic Resource Discovery (ARD), an open specification for publishing, discovering, and verifying AI capabilities across the web. Developed with partners across the industry, ARD allows tools and services to be securely shared and connected, regardless of their underlying framework, protocol, or provider.

Built with partners across the agent ecosystem

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The missing layer of the agentic web

Consider an operations agent investigating a live production incident. To resolve the issue, it might need to query observability systems, search engineering documentation, review deployment history, open support tickets, and maybe even consult specialized troubleshooting agents.

While many platforms already feature custom registries to manage these capabilities, they remain fragmented and siloed within specific ecosystems. This lack of interoperability prevents agents from easily communicating across different tools. What’s missing is a standard way for agents to discover capabilities across organizational boundaries and establish trust in what they find.

ARD provides that layer. It standardizes how capabilities are published under an organization's own domain name and indexed across federated registries, enabling any agent to dynamically find the right resources for the job. From there, ARD steps out of the way – handing off the verifiable trust metadata so the agent can establish a direct, secure connection using the tool's native protocol.

How ARD works

This architecture relies on two primitives: catalogs and registries.

  • Catalogs: To make resources discoverable, an organization publishes a catalog describing its available capabilities. Because these catalogs are hosted directly under the organization’s own domain, ownership of that domain serves as the cryptographic foundation for identity and trust.
  • Registries: Registries act as search engines for the agentic web. They crawl published catalogs, index their contents, and make them searchable. When an agent submits a discovery request, a registry returns matching capabilities along with the metadata required to verify the publisher and establish trust before connecting.
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The video below demonstrates an ARD client discovering and executing new capabilities entirely at runtime. Watch for these four key phases in the terminal window:

  1. Publishing the catalog: The provider hosts an ai-catalog.json file at a well-known path on its domain. The catalog describes the provider's available capabilities, which can include things like MCP servers, A2A agents, OpenAPI tools, or even other nested catalogs.
  2. Discovery and resolution: When a client agent needs a capability, it can either query an ARD registry using a plain-language intent (which can actively crawl and index these catalogs), or it can completely bypass search and directly fetch a catalog from a known partner's domain.
  3. Cryptographic verification: For production environments, the discovery layer allows publishers to attach verifiable trust metadata. Whether found via search or direct fetch, this enables the client agent or registry to actively confirm the publisher's true cryptographic identity before connecting to the endpoint.
  4. Direct runtime connection: The client agent dynamically loads the selected capability, interacts with it using its native protocol or API, and returns the result to the user.
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Bringing ARD to Gemini Enterprise Agent Platform

Just as the open web democratized information, ARD democratizes AI resource discovery. Google Cloud supports this with Agent Registry in Gemini Enterprise Agent Platform – the enterprise-grade product that delivers on this vision and forms part of this global federated network.

Agent Registry ensures enterprises can trust, govern, and operationalize that promise at scale. It provides fully hosted support for searching, discovering, and hosting agentic resources, including agents, skills, MCP servers and other tools. Additionally, it allows users to onboard these capabilities directly onto Agent Registry, and it will soon support authenticated publisher onboarding.

Importantly, Agent Registry plays a central role in enterprise governance. This includes assigning globally unique namespaced URNs, enforcing agentic egress policies, and pinning tools and specifications. It also handles secure resource management using Agent Identity to verify the trust manifest – ARD’s cryptographic layer for proving agent authenticity and meeting enterprise compliance standards like HIPAA.

Native support for ARD will be available in Agent Platform in the coming months, allowing organizations to securely connect their internal registries to the broader network.

Get started with ARD

The ARD specification is available now. To get involved:

  • Publish your first catalog: Follow the quickstart guide to host an ai-catalog.json file on your domain and make your services discoverable in minutes.
  • Read the specification: Check out the specification, including full schemas, federation model, trust architecture, and reference implementations.
  • Join the community: Contribute implementations, propose schema updates, and participate in the evolution of ARD by visiting our GitHub repository.

The agent ecosystem works best when it is decentralized and open. ARD is our contribution to keeping it that way. We’d love your feedback – and your catalog!

Acknowledgements

The Agentic Resource Discovery specification is licensed under Apache 2.0 and is built upon the foundational AI Catalog data model. We are grateful to the AI Catalog Working Group under the Linux Foundation, as well as the launch partners who helped shape the specification and reference implementations.

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Google and industry partners have announced the Agentic Resource Discovery (ARD) Specification, an open standard designed to enable AI agents to publish, discover, and verify external tools, APIs, and services across organizational boundaries. The specification addresses a growing gap in AI agent infrastructure, where capabilities are widely distributed but lack a common discovery and trust mechanism.

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While protocols such as the Model Context Protocol (MCP) define how an AI agent invokes a tool, ARD addresses an earlier stage in the lifecycle: how agents discover those tools in the first place. Rather than replacing existing standards, ARD is designed as a complementary discovery layer that works across frameworks and providers.

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Srinivas Krishnan, distinguished engineer at Google Cloud, highlights the motivation for ARD:

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The problem is simple to state and hard to solve, especially in the enterprise, where the answer can't just be ‘find something that works.’ It has to be governed, with security and identity built in rather than bolted on.

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The specification introduces two core constructs: catalogs and registries. Organizations publish a machine-readable ai-catalog.json file within their domain that describes available capabilities such as tools, APIs, skills, and agent endpoints. Registries aggregate these catalogs and enable agents to search based on task intent instead of relying on hardcoded integrations or static endpoint lists. This allows agents to locate relevant resources across organizational boundaries while remaining compatible with execution standards such as MCP and OpenAPI.

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How ARD Works (Source: Google Blog Post)

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Trust and verification are central to the design. ARD includes domain-based ownership and verification mechanisms so agents can validate the authenticity of discovered resources before establishing connections. This is intended to reduce risk in environments where autonomous agents may trigger actions across third-party services and enterprise systems.

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From Reddit community discussions, one perspective highlights the value of standardization:

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A uniform baseline protocol makes it easier to build alternatives without needing to interpret many different proprietary documentation formats.

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However, discussions also point out that the effectiveness of such systems will depend on the quality of exposed tools and the access or pricing models associated with them.

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The specification was developed with contributions from Microsoft, GitHub, Hugging Face, Cisco, Databricks, GoDaddy, NVIDIA, Salesforce, ServiceNow, and Snowflake. Early implementations have already emerged, including GitHub’s Agent Finder in Copilot and Hugging Face’s Discover Tool, both leveraging ARD for runtime capability discovery.

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Jennifer Marsman, principal engineer in AI at Microsoft, notes:

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The goal isn’t a single global catalog of every resource. There will be many discovery services, each defined by what it indexes, whom it serves, and how it ranks… ARD helps AI clients discover capabilities, but it doesn’t replace authentication, authorization, governance, or organizational trust decisions.

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The ARD specification is currently available with reference implementations and documentation, allowing organizations to experiment with publishing capability catalogs and exploring the federation model defined in the specification. It includes schemas, trust mechanisms, and guidelines for interoperability across discovery services. The broader ecosystem is expected to evolve through community contributions, including implementation feedback and extensions to the schema and governance model.

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About the Author

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Leela Kumili

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\n \"Anamarija
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\n Anamarija Pogorelec, Senior Staff Writer, Help Net Security\n
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Google’s open standard for AI agents to discover and verify tools

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AI agents depend on tools, skills, and other agents spread across many teams, organizations, and platforms. These capabilities live in separate systems with their own registries, and an agent working in one environment has limited means to locate and connect to a resource hosted somewhere else.

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Google addressed this gap with Agentic Resource Discovery, an open specification for publishing, discovering, and verifying AI capabilities across the web. It allows tools and services to be shared and connected across organizations regardless of their underlying framework, protocol, or provider.

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\"Agentic

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The problem ARD targets

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An operations agent investigating a production incident illustrates the case for the specification. Resolving the issue could require the agent to query observability systems, search engineering documentation, review deployment history, open support tickets, and consult specialized troubleshooting agents. Many platforms already run custom registries for such capabilities, and those registries stay fragmented and siloed within specific ecosystems. ARD supplies a standard way for agents to discover capabilities across organizational boundaries and establish trust in what they find.

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“For this ecosystem to scale, agents need reliable answers to three questions: Where does the right capability live? Which capability should I actually use? And how do I verify it’s safe to connect to?” Google Software Engineers explained.

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Catalogs and registries

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ARD relies on two building blocks. An organization publishes a catalog that describes its available capabilities, hosted as an ai-catalog.json file at a well-known path under the organization’s own domain. Ownership of that domain serves as the cryptographic foundation for identity and trust. A catalog can list MCP servers, A2A agents, OpenAPI tools, or nested catalogs.

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Registries operate as search engines for the agentic web. They crawl published catalogs, index the contents, and make them searchable. When an agent submits a discovery request, a registry returns matching capabilities along with the metadata needed to verify the publisher before any connection occurs.

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The ARD workflow moves through four stages. A provider publishes its catalog at a well-known path on its domain. An agent that needs a capability either queries a registry with a plain-language intent or fetches a catalog directly from a known partner’s domain. Publishers can attach verifiable trust metadata for production environments, letting the agent or registry confirm the publisher’s cryptographic identity before connecting. The agent then loads the capability, interacts with it through its native protocol or API, and returns the result to the user.

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Support in Gemini Enterprise Agent Platform

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Google Cloud backs the specification through Agent Registry in Gemini Enterprise Agent Platform, which forms part of the federated network. Agent Registry provides hosted support for searching, discovering, and hosting agentic resources, including agents, skills, MCP servers, and other tools. Users can onboard capabilities directly onto it, and authenticated publisher onboarding is planned.

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Agent Registry also carries enterprise governance functions. It assigns globally unique namespaced URNs, enforces agentic egress policies, and pins tools and specifications. It manages secure resources using Agent Identity to verify the trust manifest, the cryptographic layer that proves agent authenticity and meets compliance standards such as HIPAA. Native ARD support will reach Agent Platform in the coming months.

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Availability

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The ARD specification is available now under the Apache 2.0 license and builds on the AI Catalog data model from the AI Catalog Working Group under the Linux Foundation. Developers can publish a catalog using the quickstart guide, read the schemas and federation model, or contribute through the project’s GitHub repository.

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Download: The IT and security field guide to AI adoption

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