How to Use the Cloudify MCP in OpenAI Agents SDK

Manage your Cloudify infrastructure directly inside your OpenAI Agents SDK production pipeline.

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Connect Cloudify MCP to OpenAI Agents SDK

Create your Vinkius account to connect Cloudify to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cloudify with OpenAI Agents SDK

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Pull active blueprint schemas

Your agent inspects your environment by calling `get_blueprint` to extract property data. This lets your code react to infrastructure definitions without manual parsing. Everything happens in the same Python context. You avoid the overhead of custom API wrappers by letting the agent query the schema directly.

Trace deployment topologies

Use `get_deployment` to pull the current structural state of your production environment. Your agent sees exactly how components link together. This provides a live map for your logic. It prevents your agent from guessing the status of your stack when it needs to make an adjustment.

Monitor cluster workflow limits

Run `list_executions` to see exactly what is happening across your deployment bounds. Your agent identifies active limits in real time. This keeps your system state predictable. Your agent knows when to pause or push based on these hard numbers.

Setup guide

Set up Cloudify MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Cloudify tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Cloudify tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Cloudify tools and returns structured results. Copy the full example on the right to get started.

agent.py

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

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Cloudify Agent",
            instructions="You have access to Cloudify tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Cloudify MCP in OpenAI Agents SDK

You connect by using the MCPServerStreamableHttp class. Pass your Vinkius endpoint into the constructor and add the server to your Agent instance.

Yes, because the tools are auto-discovered. The safety layer checks every action before the agent sends it to your infrastructure.

Set cacheToolsList=True in your setup. This stops the agent from re-fetching the tool list every time it starts a task.

It runs over standard HTTP transport. You get consistent responses for every tool call across your entire production flow.

Cloudify never sees your keys. Vinkius manages the auth via a single token, and your agent only touches the infrastructure blueprints and execution logs.

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