How to Use the UpCloud MCP in LangChain

Q: How does UpCloud interact with LangChain for billing?

Your agent uses the getbillingsummary tool to pull monthly financial details. The output gives you specific costs and usage metrics, which your chain can then process or feed into a reporting system.

Q: Can I use LangChain to manage network resources?

Yes. You'll use tools like listnetworks to view existing SDN private networks, and then createnetwork to provision new ones as part of a larger workflow.

Q: Which data types can UpCloud provide to LangChain?

You can access operational data like server details (getserver), IP addresses (listips), and account audit logs. These are all structured inputs for your reasoning chain.

Build multi-step infrastructure reasoning pipelines using LangChain and UpCloud.

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Connect UpCloud MCP to LangChain

Create your Vinkius account to connect UpCloud 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 UpCloud with LangChain

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Claude

Perplexity

Orchestrate Infrastructure Changes with MCP Server

You can build complex workflows that chain together multiple cloud actions. Start by calling `get_account` to pull account details, then use those details to run a process like listing available resources via `list_ips`. The output of the first tool call immediately feeds into the logic for the second. This ability lets your agent decide which specific tools to sequence and in what order. For instance, you can chain together creating a network (`create_network`), then assigning an IP (`assign_ip`), and finally starting a server (`start_server`).

Automate Security and Networking Tasks

The agent handles multi-step security procedures. First, it reads current firewall rules using `list_firewall_rules`. Next, your client determines if a new rule is needed and executes the change via `create_firewall_rule`. This process ensures the network state matches the desired policy. After securing the perimeter, you can automate resource deployment. The agent might first run `get_host` to check host capacity, then decide whether to deploy a load balancer (`create_load_balancer`) or simply update existing storage configurations using `update_storage`.

Multi-Server MCP Client for LangChain

Manage resources across different cloud components in one chain. You can get details on a server using `get_server`, and if an upgrade is needed, the agent can then call `update_server`. This makes complex state transitions easy to model. Don't just provision; manage lifecycle events. The chain can stop a running machine via `stop_server`, wait a moment, and then restart it using `start_server`. You can also clone storage (`clone_storage`) as part of this multi-step data migration process.

Setup guide

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

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Common questions about UpCloud MCP in LangChain

Your agent uses the `get_billing_summary` tool to pull monthly financial details. The output gives you specific costs and usage metrics, which your chain can then process or feed into a reporting system.

Yes. You'll use tools like `list_networks` to view existing SDN private networks, and then `create_network` to provision new ones as part of a larger workflow.

You can access operational data like server details (`get_server`), IP addresses (`list_ips`), and account audit logs. These are all structured inputs for your reasoning chain.

Absolutely. You can sequence resource creation, such as building a Kubernetes cluster with `create_kubernetes_cluster` and then adding an object storage service (`create_object_storage`).

The server tracks infrastructure state, including network details (SDN private networks) and resource credentials. You can specifically export account audit logs using `export_audit_logs`.

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