Opsgenie MCP Server for LlamaIndex 11 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Opsgenie 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 Opsgenie. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in Opsgenie?"
)
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 Opsgenie MCP Server
Connect your Opsgenie account to any AI agent and take full control of your incident response workflows through natural conversation.
LlamaIndex agents combine Opsgenie tool responses with indexed documents for comprehensive, grounded answers. Connect 11 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
- Alert Management — Create, acknowledge, and close alerts. Add notes to the activity log to keep the team informed.
- Incident Coordination — Create and track major incidents with priority levels to mobilize the right people quickly.
- On-Call Visibility — Instantly check who is on-call for any schedule to route issues to the correct responder.
- Schedule Overview — List all your on-call schedules and rotations to maintain a clear view of team availability.
- Query & Audit — List alerts and incidents using powerful queries to audit past events or find active issues.
The Opsgenie MCP Server exposes 11 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 Opsgenie to LlamaIndex via MCP
Follow these steps to integrate the Opsgenie 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 11 tools from Opsgenie
Why Use LlamaIndex with the Opsgenie MCP Server
LlamaIndex provides unique advantages when paired with Opsgenie through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Opsgenie tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Opsgenie tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Opsgenie, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Opsgenie tools were called, what data was returned, and how it influenced the final answer
Opsgenie + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Opsgenie MCP Server delivers measurable value.
Hybrid search: combine Opsgenie real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Opsgenie 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 Opsgenie for fresh data
Analytical workflows: chain Opsgenie queries with LlamaIndex's data connectors to build multi-source analytical reports
Opsgenie MCP Tools for LlamaIndex (11)
These 11 tools become available when you connect Opsgenie to LlamaIndex via MCP:
acknowledge_alert
Acknowledge an alert
add_note
Add a note to an alert
close_alert
Close an alert
create_alert
Create a new Opsgenie alert
create_incident
Create a new incident
get_alert
Get details for a specific alert
get_incident
Get details for a specific incident
get_who_is_on_call
Get current on-call users for a schedule
list_alerts
List Opsgenie alerts
list_incidents
List Opsgenie incidents
list_schedules
List all on-call schedules
Example Prompts for Opsgenie in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Opsgenie immediately.
"List all currently open P1 alerts."
"Acknowledge alert 4930 and add a note saying 'I am investigating the connection pool limits'."
"Who is currently on-call for the 'SRE-Primary' schedule?"
Troubleshooting Opsgenie MCP Server with LlamaIndex
Common issues when connecting Opsgenie to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOpsgenie + LlamaIndex FAQ
Common questions about integrating Opsgenie 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 Opsgenie 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 Opsgenie to LlamaIndex
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.