How to Use the Vultr MCP in LangChain

Build multi-step Vultr infrastructure management chains with LangChain.

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Connect Vultr MCP to LangChain

Create your Vinkius account to connect Vultr 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 Vultr with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automating Bare Metal Lifecycle

You can build a chain where the agent first checks the status of all hardware using `list_bare_metals`. If it finds an instance that needs maintenance, the next step determines if it must use `halt_bare_metal` or simply run `reboot_bare_metal`. The chain runs this sequence automatically.

Account Status and BGP Setup

Need to know your account limits? Start by calling `get_account` to pull basic info. Then, the agent can check if you need specialized routing by running `get_account_bgp`. The output of these two tools feeds directly into a final step that executes `setup_bgp`, proving the link in your chain.

IP and DNS Coordination

The agent can verify existing IP assignments by calling `get_bare_metal_ipv4` for a specific instance. If it needs to change the domain visibility, the subsequent step uses that data to correctly run `set_bare_metal_ipv4_reverse`. This creates a clear, observable chain of network changes.

Setup guide

Set up Vultr 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 Vultr 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({
    "vultr-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 Vultr transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Vultr MCP in LangChain

LangChain allows your agent to treat every MCP tool call as a step in a multi-step process. It doesn't just run one command; it builds reasoning pipelines where the output of `list_bare_metals` informs whether or not the next step needs to be `start_bare_metal`. The whole thing is visible.

The MCP Server handles various resource states, including bare metal instances, API keys, and account details. Your agent's workflow determines which specific tool, like `list_applications`, provides the necessary information.

Absolutely. You can combine tools across different domains—like checking an account status (`get_account`) and then setting up BGP info (`setup_bgp`)—all within one continuous, traceable chain.

The entire execution is tracked through the chain. You get full observability via LangSmith tracing, so you see exactly which tool was called and what input/output it generated at every single step.

This MCP Server handles sensitive account information, including API keys. When your agent uses `list_api_keys`, it's dealing with the raw list of generated key IDs and associated metadata.

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