How to Use the Unbounce MCP in LangChain

Automate Unbounce workflows and build complex chains using LangChain.

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Connect Unbounce MCP to LangChain

Create your Vinkius account to connect Unbounce 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 Unbounce with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build Multi-Step Lead Funnels

Use `get_lead` to check a specific lead's details. Then, you can pipe that data into `list_forms_fields` to see what information was collected, letting your agent decide the next best action. Your AI client decides the full sequence. It might first call `list_sub_accounts`, then use the resulting ID in a call to `get_page`, creating an observable, multi-step reasoning chain.

Audit Account Structures

Start by calling `list_accounts` to get all Unbounce accounts. Next, you can run `list_domains` on a specific account ID to verify custom domains are set up correctly. This setup lets your agent aggregate data from different sources. It ensures that when your client checks the structure of an entire organization, it gathers every necessary piece before proceeding.

Analyze Page Group Contents

Need to know what pages exist under a set group? Call `list_page_groups` first. Then use `list_pages` on the resulting page group ID to get all sub-accounts. You can chain this process: identify the container (`list_page_groups`), then list its contents (`list_pages`), and finally grab specific details about a single component using `get_page`.

Setup guide

Set up Unbounce 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 Unbounce 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({
    "unbounce-alternative-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 Unbounce transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Unbounce MCP in LangChain

LangChain treats every MCP tool call as a link in a chain. You can pass the output of one function—like a list of domains from `list_domains`—directly to another tool's input, letting your agent reason through complex tasks.

Yes. Because the client supports passing tools from multiple servers, you can aggregate data from other services alongside Unbounce. This makes it easier to combine various APIs into one unified workflow.

This MCP Server exposes account metadata and conversion page details. You'll get access to lists of sub-accounts, full domain records via `list_domains`, and individual lead information using `get_lead`.

You can build a workflow that checks reports. For example, you might first call `list_leads` to get raw data, and then feed those records into another tool for processing.

The server handles account structures (sub-accounts), custom domain lists (`list_domains`), and individual lead details (`get_lead`).

Use it with your favorite AI tools

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