How to Use the Gatling MCP in Vercel AI SDK

Q: How do I stream Gatling run metrics using the Vercel AI SDK?

Use getrun inside streamText to fetch current performance data. Your frontend receives these metrics as a live stream, updating charts in real-time.

Q: Can I stop a failing Gatling test from a Vercel AI SDK chat?

Yes. Your agent calls abortsimulation using the run ID passed from your chat context. This halts the load generators instantly.

Q: How does Vercel AI SDK handle Gatling team permissions?

The server uses listteams to verify which teams are accessible. Your app passes these boundaries to the AI client to restrict simulation triggers.

Q: Can I list available test packages in my Next.js UI using this MCP Server?

Yes. This MCP Server exposes listpackages to fetch compiled simulation files directly from Gatling Enterprise.

Feed live load test data directly into your React components with Gatling and the Vercel AI SDK.

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Connect Gatling MCP to Vercel AI SDK

Create your Vinkius account to connect Gatling 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 Gatling with Vercel AI SDK

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Trigger Gatling runs via Vercel AI SDK

The `start_simulation` tool initiates load tests directly through your Vercel AI SDK chat interface without making users wait for a full page reload. Your UI receives the run ID instantly, allowing your frontend to transition immediately to a live monitoring state. By using `list_simulations` to populate a dropdown first, you let users choose their exact scenarios. The AI client handles the selection, fires the test, and updates your UI state in real-time.

Stream active test runs to your UI

The `get_run` tool polls execution status and pushes updates straight to the client. Instead of a static loading spinner, your Vercel AI SDK application renders live progress bars and active virtual user counts as the test runs. If things go sideways during a massive load test, your interface stays responsive. The user can click a button to trigger `abort_simulation` immediately, stopping runaway cloud costs before they hit your bill.

Configure pools using this MCP Server

The `list_pools` tool lists available load generator regions and capacity directly inside your edge functions. This MCP Server lets your application verify infrastructure availability before starting any heavy simulation. Combining `list_pools` with `list_packages` ensures your frontend only displays deployable test builds. Your Vercel AI SDK application can validate that the latest test package matches the correct team using `list_teams` before any runner starts.

Setup guide

Set up Gatling 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 Gatling 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 Gatling transactions",
});

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Gatling. 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 Gatling MCP in Vercel AI SDK

Use `get_run` inside `streamText` to fetch current performance data. Your frontend receives these metrics as a live stream, updating charts in real-time.

Yes. Your agent calls `abort_simulation` using the run ID passed from your chat context. This halts the load generators instantly.

The server uses `list_teams` to verify which teams are accessible. Your app passes these boundaries to the AI client to restrict simulation triggers.

Yes. This MCP Server exposes `list_packages` to fetch compiled simulation files directly from Gatling Enterprise.

Vinkius manages your API tokens securely in an isolated sandbox environment. The server uses `list_tokens` to verify credentials, but your raw secret keys never pass through to the Vercel AI SDK client or the LLM provider.

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