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 LlamaIndex 10 tools — connect in under 2 minutes

Name: Qovery
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Qovery as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Qovery. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Qovery?"
    )
    print(response)

asyncio.run(main())

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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.

LlamaIndex agents combine Qovery tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex via MCP

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

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Qovery

Why Use LlamaIndex with the Qovery MCP Server

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

Data-first architecture: LlamaIndex agents combine Qovery tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Qovery tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Qovery, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Qovery tools were called, what data was returned, and how it influenced the final answer

Qovery + LlamaIndex Use Cases

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

Hybrid search: combine Qovery real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Qovery to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Qovery for fresh data

Analytical workflows: chain Qovery queries with LlamaIndex's data connectors to build multi-source analytical reports

Qovery MCP Tools for LlamaIndex (10)

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

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

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Qovery + LlamaIndex FAQ

Common questions about integrating Qovery MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Qovery tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.