How to Use the Hevo Data (ETL & Data Pipeline) MCP in Mastra AI

Q: How do I add Hevo Data (ETL & Data Pipeline) to a Mastra AI agent?

Install @mastra/mcp and instantiate the client with your Vinkius server URL. Call mcpClient.listTools() and spread the result into your agent's tool array. Mastra auto-detects the Streamable HTTP transport.

Q: How do I build a Hevo Data (ETL & Data Pipeline) alerting workflow in Mastra AI?

Create a workflow that executes listpipelines every hour. Add a conditional branch that checks the status field of each returned pipeline. If it sees an error, the workflow routes to your notification step.

Q: Does Mastra support human-in-the-loop for these tools?

You can enable requireToolApproval for specific operations. The agent pauses execution and waits for an admin to approve the action before querying the Hevo API.

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Setup Hevo Data (ETL & Data Pipeline) with Mastra AI

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Automate Pipeline Failure Checks

`list_pipelines` and `get_pipeline` give your Mastra AI workflows the current status of every active ETL job. You configure a workflow to run these checks on a cron schedule. If a pipeline stalls, Mastra catches the failure and triggers a conditional branch. The agent can then page the data engineering team while logging the exact failure state from the Hevo API.

Track Usage Spikes in Mastra AI

`get_usage` lets your agent monitor event consumption before you hit your Hevo billing limits. You drop the MCP Server into Mastra using the standard client setup and expose the tools to your agent. The workflow pulls the event count daily. If the usage exceeds a defined threshold, Mastra executes an exponential backoff loop to verify the spike before sending an alert.

Audit Destinations and Models

`list_destinations` and `list_models` allow your workflow engine to map out where your data actually lives. The agent reads the warehouse connections and transformation models currently active in your account. You can set up a required tool approval step if the workflow needs a human to verify a new destination. This keeps your data architecture audited without manual console checks.

Setup guide

Set up Hevo Data (ETL & Data Pipeline) 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 Hevo Data (ETL & Data Pipeline) 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: "hevo-data-etl-data-pipeline-mcp-client",
  servers: {
    "hevo-data-etl-data-pipeline-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent Hevo Data (ETL & Data Pipeline) 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 Hevo Data. 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 Hevo Data (ETL & Data Pipeline) MCP in Mastra AI

Install `@mastra/mcp` and instantiate the client with your Vinkius server URL. Call `mcpClient.listTools()` and spread the result into your agent's tool array. Mastra auto-detects the Streamable HTTP transport.

Yes. If the Hevo API drops the connection while fetching workflows, Mastra's built-in engine catches the failure. It automatically retries the tool execution using exponential backoff.

Create a workflow that executes `list_pipelines` every hour. Add a conditional branch that checks the status field of each returned pipeline. If it sees an error, the workflow routes to your notification step.

You can enable `requireToolApproval` for specific operations. The agent pauses execution and waits for an admin to approve the action before querying the Hevo API.

Vinkius routes the request through an ephemeral, zero-trust container. Your Mastra agent receives the destination names and configuration metadata, but the server forgets the transaction instantly. No logs remain on our infrastructure.

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