Gatling MCP Server for Vercel AI SDK 10 tools — connect in under 2 minutes
The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Gatling through Vinkius and every tool is available as a typed function. ready for React Server Components, API routes, or any Node.js backend.
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Vinkius supports streamable HTTP and SSE.
import { createMCPClient } from "@ai-sdk/mcp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
async function main() {
const mcpClient = await createMCPClient({
transport: {
type: "http",
// Your Vinkius token. get it at cloud.vinkius.com
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
});
try {
const tools = await mcpClient.tools();
const { text } = await generateText({
model: openai("gpt-4o"),
tools,
prompt: "Using Gatling, list all available capabilities.",
});
console.log(text);
} finally {
await mcpClient.close();
}
}
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 Gatling MCP Server
Connect your Gatling Enterprise account to any AI agent and take full control of your performance testing and high-scale load simulation through natural conversation.
The Vercel AI SDK gives every Gatling tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 10 tools through Vinkius and stream results progressively to React, Svelte, or Vue components. works on Edge Functions, Cloudflare Workers, and any Node.js runtime.
What you can do
- Simulation Orchestration — List all Gatling simulations defining load scenarios and retrieve IDs, class names, and team associations natively
- Live Test Execution — Trigger new performance test runs on Gatling Enterprise infrastructure and retrieve unique run IDs flawlessly
- Test Run Monitoring — Track execution progress, statuses, and peak virtual user (VU) counts for ongoing or completed simulations synchronously
- Detailed Stats Retrieval — Access full run details including request statistics, error counts, and injection start/end times limitlessly
- Team & Quota Oversight — Enumerate teams registered in Gatling Enterprise and monitor member counts and credit quotas securely
- Artifact Management — List uploaded test packages and artifacts to verify versions and upload timestamps across your environment
- Resource Pool Auditing — Retrieve the list of load generator pools, identifying regions and instance counts to verify scaling capacity
- Autonomous Aborting — Stop all load generators for a running simulation immediately to manage system resources and prevent overruns
The Gatling MCP Server exposes 10 tools through the Vinkius. Connect it to Vercel AI SDK 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 Gatling to Vercel AI SDK via MCP
Follow these steps to integrate the Gatling MCP Server with Vercel AI SDK.
Install dependencies
Run npm install @ai-sdk/mcp ai @ai-sdk/openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the script
Save to agent.ts and run with npx tsx agent.ts
Explore tools
The SDK discovers 10 tools from Gatling and passes them to the LLM
Why Use Vercel AI SDK with the Gatling MCP Server
Vercel AI SDK provides unique advantages when paired with Gatling through the Model Context Protocol.
TypeScript-first: every MCP tool gets full type inference, IDE autocomplete, and compile-time error checking out of the box
Framework-agnostic core works with Next.js, Nuxt, SvelteKit, or any Node.js runtime. same Gatling integration everywhere
Built-in streaming UI primitives let you display Gatling tool results progressively in React, Svelte, or Vue components
Edge-compatible: the AI SDK runs on Vercel Edge Functions, Cloudflare Workers, and other edge runtimes for minimal latency
Gatling + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the Gatling MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query Gatling in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate Gatling tools and return structured JSON responses to any frontend
Chatbots with tool use: embed Gatling capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with Gatling through natural language queries
Gatling MCP Tools for Vercel AI SDK (10)
These 10 tools become available when you connect Gatling to Vercel AI SDK via MCP:
abort_simulation
Abort a running Gatling simulation
get_run
Get full details of a Gatling run
get_simulation
Get full details of a Gatling simulation
list_packages
List uploaded packages/artifacts on Gatling Enterprise
list_pools
List load generator pools on Gatling Enterprise
list_runs
List runs for a Gatling simulation
list_simulations
Simulations define load scenarios with VU populations. Returns names, IDs, class names, and team associations. List all simulations on Gatling Enterprise
list_teams
List teams on Gatling Enterprise
list_tokens
List API tokens on Gatling Enterprise
start_simulation
Returns run ID. Start a Gatling simulation run
Example Prompts for Gatling in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Gatling immediately.
"List all simulations on Gatling Enterprise"
"Start simulation 'abc-123'"
"Show me the stats for run 'run_xyz789'"
Troubleshooting Gatling MCP Server with Vercel AI SDK
Common issues when connecting Gatling to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpGatling + Vercel AI SDK FAQ
Common questions about integrating Gatling MCP Server with Vercel AI SDK.
How does the Vercel AI SDK connect to MCP servers?
createMCPClient from @ai-sdk/mcp and pass the server URL. The SDK discovers all tools and provides typed TypeScript interfaces for each one.Can I use MCP tools in Edge Functions?
Does it support streaming tool results?
useChat and streamText that handle tool calls and display results progressively in the UI.Connect Gatling 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 Gatling to Vercel AI SDK
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.