How to Use the Vertiv Environet MCP in AutoGen

Q: Can AutoGen help determine if a change is necessary?

Yes. Agents can compare data from getsensorreading() against configured limits found via getthresholds(). If the readings stay outside acceptable bounds, the agents will debate whether an adjustment using updatethreshold is warranted.

Q: How does Vertiv Environet and AutoGen manage site-specific data?

You start by listing all sites with getsites(). Then, you can assign different agents to different sites. This allows for parallel deliberation—one agent handles Site A's alerts while another debates the protocols for Site B.

Q: What steps are needed before making a critical change?

The first step must be running getsystemhealth() to confirm platform status. Then, agents should review historical data using getalerthistory() to ensure the proposed change won't conflict with past known issues.

Q: What type of data does Vertiv Environet and AutoGen track in user activity?

The getuseractivity() tool provides an audit log. When using AutoGen, agents can debate the significance of these logs, flagging unusual or unauthorized changes to thresholds for human review.

Force multi-agent debate on operational decisions for Vertiv Environet using AutoGen.

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Setup Vertiv Environet with AutoGen

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Debate alert severity with `get_active_alerts`

Don't just accept the Critical flag. Set up an agent debate: one agent acts as 'Risk Assessor' calling `get_active_alerts()` and flagging immediate dangers, while another acts as 'Operations Lead' checking surrounding data using `get_sensors()`. They must debate whether the sensor reading justifies the alert level. The system converges on a decision—a mitigated risk assessment—that is more robust than any single tool call.

Negotiate threshold changes with `update_threshold`

Thresholds shouldn't change based on one person's whim. Set up two agents: a 'Compliance Agent' that first checks the current limits using `get_thresholds()`, and a 'Forecasting Agent' that suggests new boundaries based on seasonal trends or projected load increases from `get_sites()`. They argue until they agree on the optimal, safe value. The final consensus drives the call to `update_threshold(sensorId, new_value)`.

Review system health with `get_system_health`

Before making any operational decision—like acknowledging an alert or changing a setting—the agents must first establish trust. One agent calls `get_system_health()` to verify the entire monitoring platform is stable. If the status check fails, all other actions halt immediately. The debate then shifts from 'what should we do?' to 'can we even trust this data?', making the process inherently safer.

Setup guide

Set up Vertiv Environet 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 Vertiv Environet 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="Vertiv Environet_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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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="Vertiv Environet_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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Common questions about Vertiv Environet MCP in AutoGen

AutoGen forces a debate among specialized agents. For instance, one agent might read `get_active_alerts()` while another reads `get_alert_history()`. They negotiate the root cause before suggesting an action like calling `acknowledge_alert()`.

Yes. Agents can compare data from `get_sensor_reading()` against configured limits found via `get_thresholds()`. If the readings stay outside acceptable bounds, the agents will debate whether an adjustment using `update_threshold` is warranted.

You start by listing all sites with `get_sites()`. Then, you can assign different agents to different sites. This allows for parallel deliberation—one agent handles Site A's alerts while another debates the protocols for Site B.

The first step must be running `get_system_health()` to confirm platform status. Then, agents should review historical data using `get_alert_history()` to ensure the proposed change won't conflict with past known issues.

The `get_user_activity()` tool provides an audit log. When using AutoGen, agents can debate the significance of these logs, flagging unusual or unauthorized changes to thresholds for human review.

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