How to Use the H2O.ai MCP in AutoGen

Q: Does H2O.ai expose training job data to AutoGen?

Yes, through the listjobs tool. We ensure this sensitive job data is only accessible to authorized agents within your chat session.

Debate model configurations in AutoGen by connecting H2O.ai tools to your multi-agent conversation flow.

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Connect H2O.ai MCP to AutoGen

Create your Vinkius account to connect H2O.ai to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup H2O.ai with AutoGen

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Negotiate training parameters

Let your agents debate the best model settings. One agent triggers `list_models` to see current benchmarks while another proposes configuration changes based on that data. This consensus-driven approach prevents bad model deployments. The agents verify the feasibility of their proposed changes against the current H2O.ai environment before taking action.

Automate cluster load balancing

Assign a performance agent to watch your H2O.ai cluster status. By calling `cloud_status` repeatedly, the agent can negotiate with other agents to pause or start jobs based on available compute. This keeps your cluster healthy without manual intervention. Your agents handle the trade-offs between speed and resource limits in real-time.

Verify training outcomes

Use a specialized agent to inspect job results. It uses `list_jobs` to monitor progress and `get_model` to confirm the quality of the final output. If the agents disagree on the performance metrics, they trigger a re-run or request human intervention. This setup ensures only high-quality models pass through your pipeline.

Setup guide

Set up H2O.ai MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes H2O.ai tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="H2O.ai_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent H2O.ai data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="H2O.ai_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent H2O.ai data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by H2O.ai. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about H2O.ai MCP in AutoGen

They use the tools provided by the MCP server to exchange information. Each agent can call the tools to verify its perspective during the debate.

Yes, by providing a single source of truth. If agents disagree on cluster availability, `cloud_status` provides the final answer to resolve the debate.

Yes, the adapter handles schema conversion automatically. You simply pass the tool list to your AssistantAgent to get started.

The server requires a single endpoint token. Every interaction between your agents and the H2O.ai cluster is authenticated through this secure channel.

Yes, through the `list_jobs` tool. We ensure this sensitive job data is only accessible to authorized agents within your chat session.

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