How to Use the Landbot MCP in Mastra AI

Q: How can I find a specific customer in Landbot using Mastra AI?

Your agent should use the searchcustomers tool with the customer's email. You can also use listcustomers to get a list of recent customers and then filter the results within your workflow code.

Build failure-proof Landbot workflows for customer support automation with Mastra AI. Set it and forget it.

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Connect Landbot MCP to Mastra AI

Create your Vinkius account to connect Landbot to Mastra AI 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 Landbot with Mastra AI

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Reliable Customer Routing

Build a workflow that reliably routes customers to the right place. Your agent can `search_customers` by email, and if found, use `assign_agent` to connect them to a human. Mastra's engine makes it solid. Here's the thing: if the `assign_agent` call fails—maybe no agents are online—Mastra AI can automatically retry a few times. If it still fails, your workflow can branch to a different step, like using `send_text_message` to tell the customer you'll be in touch shortly.

Automated Follow-Up Sequences

Stop letting customers fall through the cracks. This MCP Server gives your Mastra AI agent the tools to `get_messages` for a customer, check the timestamp of the last message, and decide if a follow-up is needed. If a customer has been waiting too long, the workflow automatically triggers `send_text_message` with a pre-written update. Since Mastra handles automatic retries on failure, you can be sure the message gets through.

Scheduled Bot Health Checks

You can build a Mastra AI workflow that runs on a schedule to check on your bots. The agent uses `list_bots` to get all your bots, then loops through them, calling `get_bot` on each one to inspect its details. This is great for preventative maintenance. If a bot's configuration looks wrong or it's not performing as expected, the workflow can trigger an alert in Slack or send an email. It's backend automation that just runs.

Setup guide

Set up Landbot MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Landbot tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "landbot-mcp-client",
  servers: {
    "landbot-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Landbot Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Landbot tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Landbot transactions"
);
console.log(result.text);

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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 Landbot. 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.

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Common questions about Landbot MCP in Mastra AI

In your Mastra workflow definition, add this MCP Server using your Vinkius URL. Then you can call tools like `assign_agent` and define `on_failure` steps with retries. Mastra's engine handles the state and error handling for you.

Yes. When defining your agent's tools, you can use Mastra's `requireToolApproval` option. When the agent wants to use `send_text_message`, the workflow will pause and wait for a human to approve the action before proceeding.

Mastra AI is built for this. If a tool call to the Landbot MCP server fails due to a network error or API outage, the built-in exponential backoff and retry mechanism will kick in automatically. Your workflow won't just crash.

Your agent should use the `search_customers` tool with the customer's email. You can also use `list_customers` to get a list of recent customers and then filter the results within your workflow code.

Your agent will access Landbot customer profiles (name, email) and the text from their chat history. All data access is proxied through your Vinkius endpoint, which runs each tool call in a sandboxed, zero-trust environment.

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