Vertiv Environet MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Vertiv Environet as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Vertiv Environet. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Vertiv Environet?"
)
print(response)
asyncio.run(main())
* 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 Vertiv Environet MCP Server
Connect Vertiv Environet Alert to any AI agent and gain real-time visibility into your critical infrastructure's environmental health — temperature, humidity, water leaks, smoke, and active alarms.
LlamaIndex agents combine Vertiv Environet tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Site Management — List all monitored facilities and data centers
- Sensor Monitoring — Retrieve real-time readings from temperature, humidity, and airflow sensors
- Active Alerts — View and filter active alarms by severity (Critical, Major, Minor)
- Alert Acknowledgement — Acknowledge alerts to track operator response and maintain audit trails
- Threshold Management — View and update high/low limits for environmental sensors
- Alert History — Analyze historical alarm data for root cause analysis and SLA reporting
- System Health — Verify the monitoring platform's operational status
- Audit Logs — Review user activity and configuration changes for compliance
The Vertiv Environet MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex 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 Vertiv Environet to LlamaIndex via MCP
Follow these steps to integrate the Vertiv Environet MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Vertiv Environet
Why Use LlamaIndex with the Vertiv Environet MCP Server
LlamaIndex provides unique advantages when paired with Vertiv Environet through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Vertiv Environet tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Vertiv Environet tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Vertiv Environet, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Vertiv Environet tools were called, what data was returned, and how it influenced the final answer
Vertiv Environet + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Vertiv Environet MCP Server delivers measurable value.
Hybrid search: combine Vertiv Environet real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Vertiv Environet to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Vertiv Environet for fresh data
Analytical workflows: chain Vertiv Environet queries with LlamaIndex's data connectors to build multi-source analytical reports
Vertiv Environet MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Vertiv Environet to LlamaIndex via MCP:
acknowledge_alert
Requires the alertId and the userId of the operator acknowledging it. Acknowledged alerts are removed from the "active" list and moved to history. Essential for audit trails and shift handovers. Acknowledge an active alarm to indicate it is being investigated
get_active_alerts
Can filter by severity (Critical, Major, Minor, Warning) or by site. Critical alerts often indicate immediate risk to equipment or operations. Use this to prioritize operational response. Get currently active environmental alarms and warnings
get_alert_history
Optional siteId and limit parameters. Use this for root cause analysis, SLA reporting, or identifying recurring environmental issues. View historical alarm records for analysis and reporting
get_sensor_reading
Use this for precise monitoring of critical assets (e.g., specific server rack temperature or UPS room humidity). Get the current real-time reading from a specific sensor
get_sensors
Optional siteId filters results to a specific facility. Use this to discover available monitoring points. List environmental sensors deployed across monitored sites
get_sites
Use this to identify which site IDs to use for further filtering of sensors and alerts. List all monitored sites and facilities in the Environet system
get_system_health
Use this to verify if the monitoring platform is online and functioning correctly before trusting sensor data. Check the operational status of the Environet monitoring system itself
get_thresholds
Optional sensorId filters to a specific sensor. Use this to audit current safety limits and ensure they match operational requirements. View configured alarm thresholds for sensors
get_user_activity
Use this for security auditing and operational compliance. View audit log of user actions within the Environet system
update_threshold
Changes trigger new alarms if readings cross the new boundaries. Use this to adjust sensitivity based on seasonal changes or equipment updates. Modify alarm thresholds for a sensor
Example Prompts for Vertiv Environet in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Vertiv Environet immediately.
"Show me all critical environmental alerts right now."
"What's the current temperature in Server Room B?"
"Acknowledge alert ID 98765 by operator Admin."
Troubleshooting Vertiv Environet MCP Server with LlamaIndex
Common issues when connecting Vertiv Environet to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpVertiv Environet + LlamaIndex FAQ
Common questions about integrating Vertiv Environet MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Vertiv Environet with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Vertiv Environet to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.