How to Use the Cloudify MCP in LangChain

Chain Cloudify infrastructure calls directly into your LangChain pipelines for automated cloud orchestration.

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Connect Cloudify MCP to LangChain

Create your Vinkius account to connect Cloudify to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Cloudify tool calls in LangChain

Feed `get_blueprint` outputs directly into downstream logic without manual intervention. Your agent treats every returned property as a link in a chain, building complex infrastructure workflows. Tracing every step in LangSmith shows exactly how data flows from discovery to execution. You get full visibility into how the agent sequences its calls.

Map active topologies with LangChain

Use `list_deployments` to pull current runtime states into your agent's memory. The agent consumes these structural arrays to plan its next move across your cloud environment. This creates a feedback loop where the agent knows the exact state of your cluster. It avoids guessing by working with live data provided by the MCP server.

Automate orchestration via LangChain

Trigger specific tasks using `list_executions` to track workflow bounds across your infrastructure. The agent identifies bottlenecks by analyzing active cluster limits in real-time. Your code decides the order of operations based on these results. It makes the agent a dynamic participant in managing your deployments.

Setup guide

Set up Cloudify MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Cloudify tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

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

async with MultiServerMCPClient({
    "cloudify-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Cloudify transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Cloudify MCP in LangChain

Install the MCP adapters and point your client at the Vinkius endpoint. You then pass the server tools into your agent constructor to start building chains.

The server remains stateless by default to keep things simple. You can use a persistent session object if your agent needs to maintain state between different calls.

We enforce strict transport security for every request. The integration only touches your deployment metadata and blueprint schemas, keeping your actual cloud credentials isolated.

Yes, you can aggregate multiple servers into one client. Your agent coordinates between these sources to handle complex infrastructure tasks.

It exposes blueprint schemas, deployment topologies, and node execution stats. This data is handled over encrypted channels, and we never store your private infrastructure configuration.

Use it with your favorite AI tools

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