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 LangChain 10 tools — connect in under 2 minutes

Name: Qovery
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Qovery through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "qovery": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Qovery, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

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.

LangChain's ecosystem of 500+ components combines seamlessly with Qovery through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Qovery MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Qovery via MCP

Why Use LangChain with the Qovery MCP Server

LangChain provides unique advantages when paired with Qovery through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Qovery MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Qovery queries for multi-turn workflows

Qovery + LangChain Use Cases

Practical scenarios where LangChain combined with the Qovery MCP Server delivers measurable value.

RAG with live data: combine Qovery tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Qovery, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Qovery tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Qovery tool call, measure latency, and optimize your agent's performance

Qovery MCP Tools for LangChain (10)

These 10 tools become available when you connect Qovery to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

Common issues when connecting Qovery to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Qovery + LangChain FAQ

Common questions about integrating Qovery MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.