How to Use the Unbounce MCP in LangChain
Build multi-step marketing pipelines with LangChain using Unbounce data.
Works with every AI agent you already use
…and any MCP-compatible client
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.
Audit all your landing pages in one go.
Start by listing available sites via `pages`. This gives you a map of everything running. You can then pass specific page IDs to subsequent steps, maybe checking the required domains using `domains` to ensure they're configured correctly for deployment.
Track submissions across multiple Unbounce accounts.
You can chain together actions that pull raw lead data. First, use `leads` to fetch submissions for a target page. Then, if you're worried about scope creep, you can cross-reference those leads against the available sub-accounts using `sub_accounts` to narrow down which team owns the contact record.
Manage Unbounce accounts and custom domains automatically.
Setting up a full marketing stack requires knowing your boundaries. Start by getting all available environments with `sub_accounts`. Then, you can verify if the necessary custom connections are ready by listing configured `domains`, making sure every tool call starts from a verified context.
Set up Unbounce MCP in LangChain
Prerequisites
- Python 3.10+ installed
-
langchain-mcp-adapters+langgraphpackages - 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 LangChainBaseToolobjects. - 2
Connect via HTTP transport
Use
MultiServerMCPClientwith"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
ChatOpenAIforChatAnthropic,ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
async with MultiServerMCPClient({
"unbounce-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)Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Unbounce. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
Why Choose Vinkius
Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.
Real-time monitoring
Live
visibility into every interaction
Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.
Built-in savings
60%
lower AI costs
Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.
Single dashboard
One
place for every integration
Every tool your AI connects to, managed from a single screen. One account, complete control.
Common questions about Unbounce MCP in LangChain
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
Start using the Unbounce MCP today
We host it, we monitor it, we maintain it. You just paste one token.
