How to Use the Hevo Data (ETL & Data Pipeline) MCP in OpenAI Agents SDK

Monitor your data sync status directly from your OpenAI Agents SDK production workflows using this MCP Server.

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Connect Hevo Data (ETL & Data Pipeline) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Hevo Data (ETL & Data Pipeline) to OpenAI Agents SDK 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 Hevo Data (ETL & Data Pipeline) with OpenAI Agents SDK

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Get instant status checks on your Hevo pipelines

`list_pipelines` pulls your active sync jobs straight into your agent's context. Your agent checks status fields, identifies stalled syncs, and alerts you before your warehouse goes stale. You don't have to open the browser console to see what broke. The agent calls `get_pipeline` to inspect the exact error on a failing run. Simple as that.

Watch your sync limits using this MCP Server

`get_usage` keeps your agent informed about how many events your pipelines consume. This prevents surprise bills by letting your agent halt high-volume tasks or warn the team when you approach your monthly tier limit. The agent verifies where your data lands by calling `list_destinations`. You get immediate visibility into active target warehouses without manually digging through connection settings.

Inspect transformation structures automatically

`list_models` lets your agent read the active models in your ETL workspace. The agent maps out existing transformations so you know exactly how data shifts before it hits the destination. Combine this with `list_workflows` to map dependencies across your entire data stack. Your agent analyzes these relationships to ensure downstream reports don't pull from broken sources.

Setup guide

Set up Hevo Data (ETL & Data Pipeline) MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Hevo Data (ETL & Data Pipeline) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Hevo Data (ETL & Data Pipeline) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Hevo Data (ETL & Data Pipeline) tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Hevo Data (ETL & Data Pipeline) Agent",
            instructions="You have access to Hevo Data (ETL & Data Pipeline) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Hevo Data (ETL & Data Pipeline) MCP in OpenAI Agents SDK

Install the package and use the HTTP server transport class. You pass the endpoint URL directly to your agent configuration, allowing it to discover all six pipeline monitoring tools automatically.

No. This toolset is strictly read-only for monitoring and reporting. Your agent can identify and diagnose failures, but it cannot trigger runs or modify pipeline settings.

The SDK queries the server endpoint at startup to build a schema of all available tools. Setting the caching parameter to true keeps performance high by avoiding redundant network calls.

The agent receives a clean breakdown of your active event consumption. You can write simple guardrails in your Python code to alert your team when usage spikes using this MCP setup.

No. The integration only touches pipeline configuration metadata and usage metrics. No raw database payloads ever pass through this MCP connection, keeping your customer data completely isolated.

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