How to Use the Vertiv Environet MCP in Pydantic AI

Q: Can I check thresholds using Vertiv Environet and Pydantic AI?

Yes, use getthresholds to view current safety limits for any sensor ID. You can also run updatethreshold if you need to programmatically adjust those boundaries.

Q: If I need to know which sites are monitored with Vertiv Environet and Pydantic AI?

Run the getsites tool. It returns a list of facility IDs, providing your agent with an immediate set of valid site identifiers for use in other functions.

Validate environmental data structure with Pydantic AI and Vertiv Environet.

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Connect Vertiv Environet MCP to Pydantic AI

Create your Vinkius account to connect Vertiv Environet to Pydantic AI 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 Vertiv Environet with Pydantic AI

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Respond to immediate alarms

The `get_active_alerts` tool pulls all currently active environment warnings. Your agent can filter this output by severity or site ID, ensuring the data payload is exactly what you expect. If an alarm needs processing, use `acknowledge_alert`. This function requires both the alert ID and the operator's user ID to complete the record. The response validates that these two fields are present.

Check sensor readings and locations

`get_sites` lists every monitored facility, giving you a clean set of site IDs for subsequent calls. Next, `get_sensors` lets your agent list available monitoring points at one of those sites. To get real-time metrics, run `get_sensor_reading`. This tool retrieves the current value from a specific sensor ID, which is critical data that must adhere to defined types.

Audit system state and history

Need to know if the whole monitoring platform is up? `get_system_health` checks the operational status of the Environet monitoring system. This guarantees the core data structure is valid before you rely on it. For historical context, use `get_alert_history`. It provides past alarm records that your agent can analyze without needing to query live systems.

Setup guide

Set up Vertiv Environet MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "vertiv-environet-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Vertiv Environet tools.",
)

result = await agent.run("List recent Vertiv Environet transactions")
print(result.output)

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Common questions about Vertiv Environet MCP in Pydantic AI

The server exposes tools whose outputs are validated against strict Python types. This means your agent always receives predictable, correctly structured data payloads.

Yes, use `get_thresholds` to view current safety limits for any sensor ID. You can also run `update_threshold` if you need to programmatically adjust those boundaries.

The server handles structured audit and operational logs. Specifically, the `user_activity` tool provides a clear record of user actions in the system.

Run the `get_sites` tool. It returns a list of facility IDs, providing your agent with an immediate set of valid site identifiers for use in other functions.

The server accesses user activity information. This means the system handles `user_activity` data, which documents who performs actions within the monitored environment.

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