How do I securely obtain my Qovery API Token?

Sign in to your Qovery Console. Navigate to your **Organization Settings**, then to the **API Tokens** section. Click **Generate Token** (or Add). Give it a brief name, select the desired roles, and click Create. Copy the static string immediately as it won't be shown again, and paste it to authenticate.

Can it restart specific microservices?

Yes. Once you identify the `app_id` using the list components tools, you can instruct your agent to `restart_application`. This triggers a rolling restart exactly as if you clicked 'Restart' on the console. Traffic is routed seamlessly while pods re-initialize.

What does deploy specific Git commit do?

Normally, Qovery auto-deploys a branch. With `deploy_application` you can force Qovery to pull a specific commit ID (SHA) and deploy it immediately to an environment. This is perfect for hotfixes, effectively circumventing prolonged CI loops while ensuring zero downtime.

Is this tool safe to run on production?

Yes, but with caveats. Standard queries (like listing environments and getting stats) are entirely read-only. However, tools like `restart` and `deploy` are mutating operations. Always make sure you instruct your agent precisely and maintain manual approval checkpoints before executing deployment functions.

Qovery MCP Server for Vercel AI SDK 10 tools — connect in under 2 minutes

Name: Qovery
Availability: InStock
Author: Vinkius

ai-sdk.dev/docs/ai-sdk-core/mcp

Built by Vinkius GDPR 10 Tools SDK

The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Qovery through Vinkius and every tool is available as a typed function. ready for React Server Components, API routes, or any Node.js backend.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

typescript

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 Qovery, list all available capabilities.",
    });
    console.log(text);
  } finally {
    await mcpClient.close();
  }
}

main();

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* 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 Qovery MCP Server

Connect your Qovery infrastructure to any AI agent and bring DevOps execution directly into your coding environment.

The Vercel AI SDK gives every Qovery 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

Map your Infrastructure — Traverse effortlessly through your Qovery Organizations, Projects, and Environments to build a complete mental map of your deployments
Monitor Applications — Inspect individual microservices, check active replica counts, verify auto-deploy settings, and get real-time status updates without switching contexts to the Qovery dashboard
Take Action via Chat — Trigger zero-downtime rolling restarts to cycle Kubernetes pods and refresh environment variables directly inside Claude or Cursor
Targeted Deployments — Issue a fast-track deploy of a specific Git commit SHA for hotfixes or localized feature testing, all handled friction-free by the LLM

The Qovery 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 Qovery to Vercel AI SDK via MCP

Follow these steps to integrate the Qovery 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 Qovery and passes them to the LLM

Why Use Vercel AI SDK with the Qovery MCP Server

Vercel AI SDK provides unique advantages when paired with Qovery 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 Qovery integration everywhere

Built-in streaming UI primitives let you display Qovery 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

Qovery + Vercel AI SDK Use Cases

Practical scenarios where Vercel AI SDK combined with the Qovery MCP Server delivers measurable value.

AI-powered web apps: build dashboards that query Qovery in real-time and stream results to the UI with zero loading states

API backends: create serverless endpoints that orchestrate Qovery tools and return structured JSON responses to any frontend

Chatbots with tool use: embed Qovery capabilities into conversational interfaces with streaming responses and tool call visibility

Internal tools: build admin panels where team members interact with Qovery through natural language queries

Qovery MCP Tools for Vercel AI SDK (10)

These 10 tools become available when you connect Qovery to Vercel AI SDK via MCP:

deploy_application

Triggers an immediate deployment of a specific Git commit SHA

get_application

Retrieves details for a specific Qovery application

get_environment

Retrieves details for a specific Qovery environment

get_organization

Retrieves details for a specific Qovery organization

get_project

Retrieves details for a specific Qovery project

list_applications

Lists all applications running in a specific environment

list_environments

Lists all environments (Production, Staging, etc.) in a project

list_organizations

Lists all Qovery organizations associated with the token

list_projects

Lists all projects within a Qovery organization

restart_application

Performs a zero-downtime rolling restart of a Qovery application

Example Prompts for Qovery in Vercel AI SDK

Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Qovery immediately.

"List all Qovery projects and tell me how many there are."

"Check the health and limits of the application in my staging environment."

"Deploy commit 7a8f9b2 to the backend application immediately."

Troubleshooting Qovery MCP Server with Vercel AI SDK

Common issues when connecting Qovery to Vercel AI SDK through the Vinkius, and how to resolve them.

createMCPClient is not a function

Install: npm install @ai-sdk/mcp

Qovery + Vercel AI SDK FAQ

Common questions about integrating Qovery MCP Server with Vercel AI SDK.

How does the Vercel AI SDK connect to MCP servers?

Import createMCPClient from @ai-sdk/mcp and pass the server URL. The SDK discovers all tools and provides typed TypeScript interfaces for each one.