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 OpenAI Agents SDK 10 tools — connect in under 2 minutes

Name: Qovery
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 10 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Qovery through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

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.

python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Qovery Assistant",
            instructions=(
                "You help users interact with Qovery. "
                "You have access to 10 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Qovery"
        )
        print(result.final_output)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 OpenAI Agents SDK auto-discovers all 10 tools from Qovery through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Qovery, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

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 OpenAI Agents 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 OpenAI Agents SDK via MCP

Follow these steps to integrate the Qovery MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 10 tools from Qovery

Why Use OpenAI Agents SDK with the Qovery MCP Server

OpenAI Agents SDK provides unique advantages when paired with Qovery through the Model Context Protocol.

Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Qovery + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Qovery MCP Server delivers measurable value.

Automated workflows: build agents that query Qovery, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries Qovery, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Qovery tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Qovery to resolve tickets, look up records, and update statuses without human intervention

Qovery MCP Tools for OpenAI Agents SDK (10)

These 10 tools become available when you connect Qovery to OpenAI Agents 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 OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents 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 OpenAI Agents SDK

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

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Qovery + OpenAI Agents SDK FAQ

Common questions about integrating Qovery MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.