IBM QRadar 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 IBM QRadar 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 IBM QRadar. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in IBM QRadar?"
)
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 IBM QRadar MCP Server
Connect IBM QRadar to any AI agent via MCP.
How to Connect IBM QRadar to LlamaIndex via MCP
Follow these steps to integrate the IBM QRadar 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 IBM QRadar
Why Use LlamaIndex with the IBM QRadar MCP Server
LlamaIndex provides unique advantages when paired with IBM QRadar through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine IBM QRadar tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain IBM QRadar tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query IBM QRadar, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what IBM QRadar tools were called, what data was returned, and how it influenced the final answer
IBM QRadar + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the IBM QRadar MCP Server delivers measurable value.
Hybrid search: combine IBM QRadar real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query IBM QRadar 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 IBM QRadar for fresh data
Analytical workflows: chain IBM QRadar queries with LlamaIndex's data connectors to build multi-source analytical reports
IBM QRadar MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect IBM QRadar to LlamaIndex via MCP:
execute_aql
Returns a search ID for async retrieval. Execute an Ariel Query Language (AQL) search
get_aql_results
Get results from a completed AQL search
get_aql_status
Get the status of an AQL search
get_log_sources
List QRadar log sources
get_network_hierarchy
List QRadar network hierarchy
get_offense_details
Get details for a specific QRadar offense
get_offenses
List QRadar offenses
get_reference_sets
). List QRadar reference sets
get_rules
List QRadar correlation rules
update_offense
Update a QRadar offense
Troubleshooting IBM QRadar MCP Server with LlamaIndex
Common issues when connecting IBM QRadar to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpIBM QRadar + LlamaIndex FAQ
Common questions about integrating IBM QRadar 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 IBM QRadar 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 IBM QRadar to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.