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Vinkius

H2O.ai Connector for AI agents.

6 live capabilities

Manage machine learning models and monitor training jobs in your cloud instance.

Live agent request H2O.ai / Connector

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AI Agent

Why people use H2O.ai

H2O.ai Machine Learning Model Management

This Connector changes that by bringing the dashboard to your chat. You can simply ask your agent to list models or check the status of a training job. You get the data you need instantly, which means you spend less time monitoring and more time building.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get a conversational interface for your entire H2O.ai machine learning environment.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Checking training job progress

    An ML engineer asks their agent if the XGBoost training job is finished.

  2. Real-world use case 02

    Verifying data frame columns

    A data scientist wants to see if the new data frame has the correct headers.

  3. Real-world use case 03

    Auditing model versions

    A product lead needs to know which models are available for production.

Complete set · 6capabilities

The complete H2O.ai capability set.

These are the exact actions your AI can choose when you ask it to work with H2O.ai.

Capability set01 / 02

01—03

3 capabilities in this set.

Part of 6 available through H2O.ai.

  1. 01 Capability

    Get frame

    Retrieve specific data from a loaded frame to check column mappings.

  2. 02 Capability

    List models

    See all machine learning models you have previously generated in your instance.

  3. 03 Capability

    Get model

    Pull detailed configuration and performance metrics for a specific model.

Capability set02 / 02

04—06

3 capabilities in this set.

Part of 6 available through H2O.ai.

  1. 04 Capability

    List jobs

    See a list of all ongoing and completed training tasks on your cluster.

  2. 05 Capability

    Cloud status

    Check the health and memory utilization of your cloud cluster.

  3. 06 Capability

    List frames

    See all data frames currently loaded in your H2O clusters.

Set up in minutes

One URL. Then ask H2O.ai to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use H2O.ai from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_joJzJEG0t6WyiAGy91nhlV2uG13UXBw7Ejke9uIr/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it H2O.ai, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable H2O.ai for the conversation.

Where the request belongs

Work H2O.ai can move forward.

Built around the request

This is for the data scientist or ML engineer who is tired of clicking through multiple dashboard tabs just to see if a training job finished or if the cluster is still healthy.

01

Data Scientist

Verifies frame data and schema mappings during the preprocessing phase without leaving the chat interface.

02

ML Engineer

Audits model architectures and tracks deployment statuses across various cloud instances.

03

Product Manager

Monitors cluster health and model availability to ensure the production pipeline is operational.

Build the capability set

Add more capabilities.

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04 1 capability

Modelbit (ML Model Deployments)

Deploy and call machine learning models directly from your AI agent using Modelbit's inference endpoints.

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05 6 capabilities

Comet ML

Manage machine learning experiments via Comet. track model metrics, audit project workspaces, and inspect ML run parameters directly from any AI agent.

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MindsDB (AI Database & Predictors)

Manage AI-powered data via MindsDB. execute SQL predictions, audit ML models, and connect data sources.

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Bring your own AI

Change the model, client or framework. Keep H2O.ai connected.

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Before you connect

Questions about H2O.ai.

The practical details behind the request, access and result.

How does the H2O.ai MCP help my machine learning workflow?

It gives you a conversational way to interact with your H2O.ai instance. You can check model versions, see training job statuses, and monitor cluster health without leaving your AI client.

Can I use H2O.ai MCP to see if my cluster is running out of memory?

Yes. You can ask your agent to check the cloud status, and it will report the current memory utilization and hardware health of your cluster.

Does the H2O.ai MCP support tracking training jobs?

It does. You can query your training jobs to see which are ongoing, which are queued, and how much progress has been made on long-running tasks.

How do I connect my H2O.ai instance to my AI agent?

First, subscribe to the Connector. Then, provide your H2O.ai Base URL, which you can find in your cluster settings or cloud dashboard.

Can I see specific data columns in my H2O.ai frames?

Yes, you can retrieve specific dimensional data and column mappings from any loaded data frame using natural language commands.

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.

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.

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.

One connection away

Give your agent a direct line to H2O.ai.

Connect H2O.ai once. Keep it beside 5,900+ managed Connectors when the next task needs more.

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