AirOps 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 AirOps as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 AirOps. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in AirOps?"
)
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 AirOps MCP Server
Connect your AirOps account to your AI agent to unlock professional AI workflow orchestration and agent management. From executing complex multi-step workflows synchronously or asynchronously to interacting with specialized chat agents and managing managed memory stores, your agent handles your AI operations through natural conversation.
LlamaIndex agents combine AirOps 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
- Workflow Orchestration — Execute and monitor AirOps apps and workflows, passing custom parameters and retrieving structured results
- Agent Interaction — Chat directly with your specialized AirOps agents to perform niche tasks or leverage unique agent instructions
- Memory Management — Search within managed memory stores (vector databases) and add documents to enrich your AI's domain knowledge
- File Orchestration — Upload and manage files to be used as inputs for your AI workflows and data extraction tasks
- Real-time Status — Monitor execution statuses and cancel long-running AI tasks directly from your chat interface
The AirOps 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 AirOps to LlamaIndex via MCP
Follow these steps to integrate the AirOps 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 AirOps
Why Use LlamaIndex with the AirOps MCP Server
LlamaIndex provides unique advantages when paired with AirOps through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine AirOps tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain AirOps tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query AirOps, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what AirOps tools were called, what data was returned, and how it influenced the final answer
AirOps + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the AirOps MCP Server delivers measurable value.
Hybrid search: combine AirOps real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query AirOps 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 AirOps for fresh data
Analytical workflows: chain AirOps queries with LlamaIndex's data connectors to build multi-source analytical reports
AirOps MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect AirOps to LlamaIndex via MCP:
add_memory_document
Enrich AI knowledge
cancel_execution
Stop a running task
chat_with_agent
Interact with AI agent
execute_workflow_async
Run workflow asynchronously
execute_workflow_sync
Best for quick tasks. Run workflow synchronously
get_app_details
Get app metadata
get_execution_status
Check execution progress
list_apps
List AI applications
search_memory_store
Search vector database
upload_file
Upload file for AI
Example Prompts for AirOps in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with AirOps immediately.
"List all AI apps in my AirOps workspace."
"Execute the 'Data Extractor' app (UUID: abc-123) with input 'Extract names from this text: John Doe visited London'."
"Search my 'Knowledge Base' memory store for 'API integration guides'."
Troubleshooting AirOps MCP Server with LlamaIndex
Common issues when connecting AirOps to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAirOps + LlamaIndex FAQ
Common questions about integrating AirOps 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 AirOps 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 AirOps to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.