DataRobot now supports the Agentic Resource Discovery Specification, making DataRobot Agent Skills easier for AI clients, registries, and developers to find.
Agents are only as useful as the capabilities they can reach.
A coding agent can write code. A workflow agent can call tools. An enterprise agent can reason across systems. But all of that depends on the same basic question: when the agent needs a capability, how does it find the right one?
Until now, the answer has mostly been manual. Developers wire in MCP servers, install skills, point agents at docs, and maintain long lists of tools that may or may not be relevant to the task at hand. That works for a small number of hand-picked integrations. It breaks down when every platform, team, and community is publishing new agentic resources.
That is why we are excited to share that DataRobot now supports the Agentic Resource Discovery Specification, also known as ARD.
DataRobot now publishes an ARD-compatible AI catalog for DataRobot Agent Skills and MCP Servers, making those skills and MCPs discoverable from our domain through the standard .well-known/ai-catalog.json path at https://datarobot.com/.well-known/ai-catalog.json
Why ARD matters
Agentic Resource Discovery is an open specification for publishing, discovering, and verifying agentic resources across the web. Those resources can include skills, MCP servers, APIs, agents, tools, workflows, and other capabilities.
The model is simple: providers publish a catalog of available resources under their own domain. Discovery services and AI clients can then find, index, and resolve those resources when an agent needs them.
That matters because the agent ecosystem is moving from static wiring to dynamic discovery.
Instead of asking developers to preload every possible tool and skill into an agent’s context, ARD gives agents and registries a standard way to discover the right capability for the task. The agent can search, select, and connect to relevant resources without carrying every integration by default.
For enterprises, that discovery layer is especially important. Teams need agents that can find useful capabilities, but they also need control over what gets surfaced, where it comes from, and how it is governed.
What DataRobot is publishing
DataRobot’s ARD catalog currently points to DataRobot Agent Skills and MCPs.
This includes skills for:
Model training
Model deployment
Predictions and batch scoring
Feature engineering
Model monitoring
Model explainability
Data preparation
App Framework CI/CD
External agent monitoring
Agent Assist
These skills package DataRobot platform knowledge into task-scoped context that coding agents can use directly. They help agents understand DataRobot workflows, SDK patterns, deployment steps, validation checks, and observability practices.
In other words, they teach agents how to use DataRobot correctly.
With ARD support, those skills are not only available in repositories and agent environments. They are also published in a standard catalog that discovery tools can crawl, index, and resolve.
From installable skills to discoverable platform context
We have been investing in DataRobot Skills and MCPs because agents need more than documentation. They need operational context.
A human developer can read docs, infer missing steps, ask a teammate, and recover when an API call fails. An agent needs the right context at the right moment. Otherwise, it guesses.
Skills and MCPs reduce that guesswork by giving agents precise instructions for common platform workflows. ARD takes the next step by making those resources easier to find.
That shift matters for developer experience. It also matters for platform teams.
If you are building agents on DataRobot, you should not have to manually teach every tool where DataRobot skills live. If you are building an AI client or registry, you should have a standard way to discover DataRobot resources. If you are governing agentic AI inside an enterprise, you should be able to decide which catalogs and registries your agents can use.
ARD gives the ecosystem a path toward that model.
Try it
Learn more through the official ARD specification.
Get started today: Follow the quickstart guide to publish your AI catalog in minutes
Build: Create your first DataRobot Agent
Read: Learn more about DataRobot Skills, MCP, and the agentic developer surface
What comes next
Agentic discovery is still early, and the specification is moving quickly. That is exactly why we wanted DataRobot to participate now.
The agentic web will not be built from one marketplace, one vendor catalog, or one hard-coded tool list. It will need open discovery, clear ownership, and resources that agents can actually use.
DataRobot’s role is to make enterprise AI agents easier to build, operate, monitor, and govern. Supporting ARD is another step toward that future: DataRobot platform context that is not just available, but discoverable.
Agents should not have to guess where the right capability lives.
Now, they can find DataRobot.
The post DataRobot Agent Skills are now discoverable through Agentic Resource Discovery appeared first on DataRobot.