How to Use the Landing MCP in LangChain

Q: How do I connect the Landing MCP Server to LangChain?

Install langchain-mcp-adapters. Initialize a MultiServerClient pointing to the server URL, call client.gettools(), and pass the array to your ReAct agent.

Q: Why is my agent failing to delete a webhook?

Your agent is likely passing a malformed ID to deletelandingwebhook. Check your LangSmith logs to verify the exact string the agent generated.

Q: How is my lead data secured during execution?

This integration accesses raw customer contact info via listlandingleads. Vinkius runs the server in an isolated V8 sandbox that destroys itself after execution, ensuring your lead data never leaks to other tenants.

Build multi-step conversion pipelines. Connect this MCP Server to LangChain to track leads and configure webhooks instantly.

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Connect Landing MCP to LangChain

Create your Vinkius account to connect Landing 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 Landing with LangChain

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Claude

Perplexity

Chain Landing tools in LangChain

Your ReAct agent needs live Landing data to make decisions. The Landing MCP Server exposes seven endpoints directly to your LangChain setup. You can pull a list of active campaigns using `list_landing_projects` and immediately pass that context into your next prompt. Chaining actions creates real value. Have your agent read incoming data with `list_landing_leads`, evaluate the conversion rate, and automatically spin up a new notification route via `create_landing_webhook`. Every step logs straight to LangSmith for full tracing.

Audit campaigns with tool calling

Stop manually checking your Landing dashboard. You can wire a Python script to run `list_landing_pages` every morning and feed the output into a summarization chain. The agent reads the raw JSON and tells you exactly which pages are live. If something looks wrong, the agent digs deeper. It runs `get_my_landing_profile` to check account status or pulls `list_landing_webhooks` to ensure your lead routing remains intact.

Automate lead routing

Landing lead data decays fast. When you attach this MCP Server to your graph, you give your agent the ability to control data flow. It detects a new campaign and instantly fires `create_landing_webhook` to pipe those leads into your CRM. When a campaign ends, the agent cleans up after itself. It calls `delete_landing_webhook` so you avoid wasting API calls on dead endpoints. You write the logic once, and the agent handles the infrastructure.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Landing. 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

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Common questions about Landing MCP in LangChain

Install `langchain-mcp-adapters`. Initialize a MultiServerClient pointing to the server URL, call `client.get_tools()`, and pass the array to your ReAct agent.

Yes. Because the tools run through standard tool calling, every request to `list_landing_leads` or `list_landing_pages` shows up in your LangSmith traces with exact latency and token counts.

The tools themselves are stateless. If you want your agent to remember which pages it saw from `list_landing_projects`, you need to use `client.session()` to maintain that context across chain executions.

Your agent is likely passing a malformed ID to `delete_landing_webhook`. Check your LangSmith logs to verify the exact string the agent generated.

This integration accesses raw customer contact info via `list_landing_leads`. Vinkius runs the server in an isolated V8 sandbox that destroys itself after execution, ensuring your lead data never leaks to other tenants.

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