How to Use the IBM QRadar MCP in Vercel AI SDK

Q: Can I update offense statuses in IBM QRadar directly from my Vercel AI SDK frontend?

Yes, you can bind UI buttons to agent actions that call updateoffense. The state change executes instantly in QRadar, and the updated offense details stream back to your React components.

Q: How do we verify network context in IBM QRadar using Vercel AI SDK UI components?

Your agent calls getnetworkhierarchy to fetch the defined IP subnets and maps them to your visual network graph. This lets analysts see whether an offending IP is internal or external right inside the chat interface.

Q: What is the setup process for this MCP integration?

Install the packages using npm, initialize the client with createMCPClient, and pass the tools to streamText. Always call mcpClient.close() at the end of the execution block to prevent memory leaks.

Stream real-time IBM QRadar offense updates and log query results directly into your Vercel AI SDK frontend.

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Connect IBM QRadar MCP to Vercel AI SDK

Create your Vinkius account to connect IBM QRadar to Vercel AI SDK 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 IBM QRadar with Vercel AI SDK

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Live Ariel query streaming with Vercel AI SDK

Stop making SOC analysts stare at loading spinners while waiting for massive SIEM searches. This MCP Server lets your Next.js frontend run asynchronous Ariel searches on the fly. Your agent starts the search via `execute_aql`, polls the progress with `get_aql_status`, and streams the raw log rows back using `get_aql_results` the second they land. You build the UI to render these logs as they arrive. No waiting for a giant JSON payload to compile on the backend. The analyst gets immediate eyes on the threat vector.

Direct offense triage inside your custom React UI

Pull active security alerts straight into your application layout without clunky page refreshes using this MCP client. Your agent grabs the queue using `get_offenses` and fetches deep context with `get_offense_details` to build a clean threat feed. When an analyst makes a decision, they update the status instantly. The Vercel AI SDK sends the payload to `update_offense` behind the scenes, changing the state in QRadar and updating the React state in one clean cycle.

Edge-compatible network and log source discovery

Run your security dashboard on Vercel Edge Functions without hitting cold start bottlenecks. The agent queries your system architecture with `get_network_hierarchy` and `get_log_sources` to map out anomalous IP addresses or silent loggers. Because this MCP Server handles the heavy lifting, your edge middleware stays lightweight. It maps network segments directly to your visual components in milliseconds.

Setup guide

Set up IBM QRadar MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all IBM QRadar tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent IBM QRadar transactions",
});

console.log(text);
await mcpClient.close();

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by IBM QRadar. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about IBM QRadar MCP in Vercel AI SDK

The SDK uses asynchronous polling to keep your serverless functions from timing out. Your agent initiates the search using `execute_aql`, checks status via `get_aql_status`, and streams the chunks from `get_aql_results` once they are ready.

Yes, you can bind UI buttons to agent actions that call `update_offense`. The state change executes instantly in QRadar, and the updated offense details stream back to your React components.

Your agent calls `get_network_hierarchy` to fetch the defined IP subnets and maps them to your visual network graph. This lets analysts see whether an offending IP is internal or external right inside the chat interface.

Install the packages using npm, initialize the client with `createMCPClient`, and pass the tools to `streamText`. Always call `mcpClient.close()` at the end of the execution block to prevent memory leaks.

Your raw Ariel logs and offense data stay inside the Vinkius V8 sandbox during execution. This setup avoids transmitting sensitive IP addresses or log payloads to external servers.

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