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Connect H2O.ai to ChatGPT, Claude and Gemini

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Run your machine learning lifecycle through a chat interface.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

A futuristic MCP client docking bay
V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 6 capabilities

We ship only the actions that matter.

Capability set01 / 02

01-03

3 capabilities in this set.

Part of 6 available through H2O.ai.

  1. 01

    Get frame

    This capability retrieves specific dimensional data from a loaded H2O frame. Use it to pull column mappings or data subsets for inspection.

  2. 02

    List models

    This capability pulls a list of all machine learning models stored in your H2O cluster. It is the fastest way to see what you have trained.

  3. 03

    List jobs

    This capability shows the status of all training jobs and tasks queued on the cluster. It helps you track execution progress.

Capability set02 / 02

04-06

3 capabilities in this set.

Part of 6 available through H2O.ai.

  1. 04

    List frames

    This capability provides a list of all structured datasets currently loaded into your H2O clusters.

  2. 05

    Get model

    This capability fetches detailed configuration and performance data for a specific model. Use it to audit deployment parameters.

  3. 06

    Cloud status

    This capability checks the health of your H2O cloud cluster. It returns hardware architecture status and memory usage details.

Real work

What teams actually run

01

Real-time Job Monitoring

Check if a heavy XGBoost training job is still running or if it has failed without logging into the cloud console.

02

Data Schema Verification

Quickly inspect the columns and structure of a loaded data frame to ensure it matches your model requirements.

03

Cluster Health Checks

Verify that your H2O instance has enough available memory before starting a large scale training task.

04

Model Performance Audits

Compare different model versions by pulling their specific metrics through your AI agent.

Automate a task

Put H2O.ai to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

H2O.ai works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put H2O.ai to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

797ms average. Fast in production.

H2O.ai is checked daily against the live service.

Daily averagePeak 986ms
Oct 3Today
Fastest day
655ms
Slowest day
986ms
14-day trend
Slowing+26%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 6 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of H2O.ai, so you can see the experience inside your AI.

It does not authenticate your account with H2O.ai. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

H2O.ai Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_joJzJEG0t6WyiAGy91nhlV2uG13UXBw7Ejke9uIr/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — H2O.ai capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "h2oai-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_joJzJEG0t6WyiAGy91nhlV2uG13UXBw7Ejke9uIr/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up H2O.ai in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work H2O.ai owners hand off.

This MCP is built for technical professionals managing machine learning workflows who want to reduce dashboard fatigue.

  • 01

    Data Scientists

    They use it to monitor training jobs and verify frame data without manual dashboard checks.

  • 02

    ML Engineers

    They use it to audit model architectures and track deployment statuses via natural language.

  • 03

    Developers

    They use it to test H2O.ai integrations and verify data schemas through a chat interface.

FAQ

Questions H2O.ai owners ask.

  • 01

    How do I connect this MCP to my H2O.ai instance?

    You just need to provide your H2O.ai Base URL, which you can find in your H2O cluster settings or cloud dashboard, during the setup process.

  • 02

    Which AI clients can I use with this MCP?

    This MCP works with any compatible client, including Claude, Cursor, Windsurf, and VS Code.

  • 03

    Can I monitor my cluster's hardware usage?

    Yes, you can use the cloud status capabilities to check hardware architecture health and memory utilization within your H2O instances.

  • 04

    Is it possible to see the progress of training jobs?

    Yes, you can query the timeline of training jobs and tasks queued on your cluster to see exactly how they are progressing.

  • 05

    Does this MCP work with H2O.ai cloud?

    Yes, it is designed to connect to your H2O.ai instances, including cloud-based clusters.

  • 06

    Can my agent list all data frames currently loaded in my H2O cluster?

    Yes. Use the 'list_frames' capability. The agent retrieves the list of structured datasets securely loaded into memory, including their IDs and basic metadata, allowing you to browse available data flawlessly.

  • 07

    How do I check the progress of a model training job via chat?

    Use the 'list_jobs' capability. Your agent will query the timeline nodes tracking all long-running tasks on the cluster, providing you with the current execution status and progress percentages synchronously.

  • 08

    Can I see the internal architecture and metrics of a model through the agent?

    Absolutely. Use the 'get_model' capability with the specific model ID. The agent will fetch the detailed configuration blocks, exposing hyperparameters and performance metrics natively within your chat context.

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