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

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

H2O.ai MCP Server for OpenAI Agents SDK 6 tools — connect in under 2 minutes

Name: H2O.ai
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 6 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect H2O.ai through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="H2O.ai Assistant",
            instructions=(
                "You help users interact with H2O.ai. "
                "You have access to 6 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from H2O.ai"
        )
        print(result.final_output)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About H2O.ai MCP Server

Connect your H2O.ai instance to any AI agent and take full control of your machine learning lifecycle, automated data processing, and cluster monitoring through natural conversation.

The OpenAI Agents SDK auto-discovers all 6 tools from H2O.ai through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries H2O.ai, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

What you can do

Data Frame Orchestration — List structured datasets securely loaded into H2O clusters and retrieve specific dimensional data mapping explicit frame columns natively
Model Inventory Auditing — Iterate through tracked machine learning models previously generated inside your cloud instance to verify performance metrics and versions
Inference Monitoring — Access detailed configuration blocks for active model architectures to verify deployment boundaries and parameters synchronously
Training Job Oversight — Query timeline nodes tracking long-running tasks and model training jobs queued on the cluster to monitor execution progress
Cloud Cluster Auditing — Ping root endpoints defining hardware architecture health and memory utilization within your H2O instances flawlessly
MLOps Command Center — Verify available frames and models to orchestrate complex data science workflows and model evaluations using natural language
Status Verification — Identify precise executing statuses of ongoing jobs to ensure your AI pipeline is operational and within resource limits securely

The H2O.ai MCP Server exposes 6 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect H2O.ai to OpenAI Agents SDK via MCP

Follow these steps to integrate the H2O.ai MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 6 tools from H2O.ai

Why Use OpenAI Agents SDK with the H2O.ai MCP Server

OpenAI Agents SDK provides unique advantages when paired with H2O.ai through the Model Context Protocol.

Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

H2O.ai + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the H2O.ai MCP Server delivers measurable value.

Automated workflows: build agents that query H2O.ai, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries H2O.ai, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through H2O.ai tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query H2O.ai to resolve tickets, look up records, and update statuses without human intervention

H2O.ai MCP Tools for OpenAI Agents SDK (6)

These 6 tools become available when you connect H2O.ai to OpenAI Agents SDK via MCP:

cloud_status

Get cloud status

get_frame

Get frame

get_model

Get model

list_frames

List frames

list_jobs

List jobs

list_models

List models

Example Prompts for H2O.ai in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with H2O.ai immediately.

"List all machine learning models in my H2O cluster"

"What is the current status of the H2O cloud cluster?"

"Show me the last 3 training jobs"

Troubleshooting H2O.ai MCP Server with OpenAI Agents SDK

Common issues when connecting H2O.ai to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

H2O.ai + OpenAI Agents SDK FAQ

Common questions about integrating H2O.ai MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.