# Hevo Data (ETL & Data Pipeline) MCP for AI Agents AI Agent Connect

> Hevo Data (ETL & Data Pipeline) MCP lets you manage your automated data integration and ETL orchestration through natural conversation. You can list pipelines, monitor warehouse destinations like BigQuery and Snowflake, track transformation models, and audit your account usage and billing limits. It's the fastest way to keep your data stack in sync without manually checking multiple dashboards.

## Overview
- **Category:** developer-tools
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_g8OahZ0tlknHo4zGlDARjBa11ENx2y9HNf6P2vgT/ai-agent-connect
- **Tags:** etl, data-pipelines, data-warehousing, data-integration, pipeline-monitoring, automated-sync

## Description

Managing a complex data stack usually means jumping between dozens of tabs to check if your pipelines are actually running. You're constantly hunting for that one specific mapping or trying to figure out why a row replication hit a snag in your warehouse. This Hevo Data MCP changes that by letting your agent handle the heavy lifting. Instead of manual digging, you can just ask your agent to show you the status of your ETL runs or check if your staging logic is still holding up. It gives you a direct line to your data orchestration, making it easy to spot bottlenecks before they break your reports. Because Vinkius hosts this along with thousands of other integrations, you can get this connected once and have your agent ready to manage your entire data flow across BigQuery, Snowflake, and Redshift. It turns a tedious monitoring job into a simple conversation, so you can spend less time troubleshooting and more time actually analyzing the data.

## Tools

### list_pipelines
See every ETL pipeline in your Hevo account. This helps you get a bird's-eye view of your data flows.

### get_pipeline
Pull specific details for a single pipeline. Use this to check exact routing and frequency for a specific ID.

### list_destinations
See all your warehouse targets like BigQuery or Snowflake. This is great for verifying where data is being sent.

### list_models
Review all your transformation models. It helps you ensure your staging logic remains consistent.

### list_workflows
View the DAG workflows connecting your transformations. This shows how your data moves across the stack.

### get_usage
Check your current row counts and billing limits. It's the easiest way to stay on top of your account health.

## Prompt Examples

**Prompt:** 
```
List all my active pipelines.
```

**Response:** 
```
| Pipeline Name | ID | Status |
| :--- | :--- | :--- |
| Stripe to BigQuery | pipe-001 | Healthy |
| HubSpot to Snowflake | pipe-005 | Running |
| Shopify to Redshift | pipe-008 | Healthy

I've found 3 active pipelines. Would you like to see the details for any of these?
```

**Prompt:** 
```
Where is my 'Sales Data' pipeline sending info?
```

**Response:** 
```
The 'Sales Data' pipeline is currently replicating to 2 destinations:

* **BigQuery** (Production Dataset)
* **Snowflake** (Staging Warehouse)

Both are currently showing a **Healthy** status.
```

**Prompt:** 
```
How many rows have we used this month?
```

**Response:** 
```
You've used **12.5 million rows** out of your **20 million row** monthly quota (62.5%). 

At current ingestion rates, you are projected to finish the month at 18.2 million rows. I can break down usage by pipeline if needed.
```

## Capabilities

### List active pipelines
See every ETL pipeline in your account at a glance.

### Check pipeline details
Get specific metadata and routing info for a single pipeline.

### Monitor warehouse destinations
See where your data is landing, from BigQuery to Snowflake.

### Track transformation models
Keep tabs on the staging logic and mappings for your data.

### View orchestration workflows
See how your transformations connect across your data stack.

### Audit account usage
Check your row counts and billing limits in real-time.

## Use Cases

### Troubleshooting a failed sync
A data engineer asks the agent to check the status of a specific pipeline to see why data isn't hitting Snowflake.

### Pre-reporting audit
An analytics lead asks the agent to list all transformation models to ensure the staging logic is ready for the weekly report.

### Budget monitoring
An ops manager asks the agent for a usage report to see how many rows were replicated this month.

### Pipeline discovery
A new team member asks the agent to list all active pipelines to understand the current data flow.

## Benefits

- Stop manual dashboard hopping by seeing every active data flow in one place.
- Quickly verify warehouse health by checking BigQuery, Snowflake, and Redshift statuses in one view.
- Keep your data quality high by reviewing transformation mappings and staging logic instantly.
- Stay under budget by monitoring row replications and billing ceilings in real-time.
- Map out your entire data stack by seeing how transformations connect across your infrastructure.

## How It Works

The bottom line is you get a conversational interface for your entire Hevo data infrastructure.

1. Subscribe to the Hevo Data MCP on Vinkius.
2. Provide your Hevo Data API Key and Region (US, EU, AU, or IN).
3. Ask your agent to list pipelines, check statuses, or pull usage metrics.

## Frequently Asked Questions

**Can the Hevo Data MCP help me check if my data is reaching Snowflake?**
Yes, you can ask your agent to list your destinations. It will show you the status of all warehouse targets, including Snowflake, BigQuery, and Redshift.

**How do I see my row usage limits using the Hevo Data MCP?**
Just ask your agent for your usage metrics. It will pull your current row counts and billing ceilings in real-time so you can stay on budget.

**Can I see all my ETL pipelines at once with this Connector?**
Yes, your agent can pull a full list of all your active pipelines, including their IDs and current statuses, in one command.

**Does the Hevo Data MCP work for BigQuery and Redshift?**
Yes, it supports monitoring for multiple warehouse destinations, including BigQuery and Redshift, alongside Snowflake.

**Can I use this to check my transformation models?**
Yes, you can ask your agent to list your models to verify that your staging logic and mappings are correct.

**Can I check the status of my data destinations through my agent?**
Yes. Use the `list_destinations` tool to see all your warehouse targets. Your agent will provide the status and details of where your data is being replicated, ensuring delivery to platforms like BigQuery or Snowflake.

**How do I find a specific pipeline's configuration?**
Use the `get_pipeline` tool with a unique Pipeline ID to extract explicit routing mappings and ingestion frequencies. This is perfect for auditing specific ETL flows without manual searching.

**Can I monitor my account's row usage through a conversation?**
Absolutely. The `get_usage` tool retrieves real-time account usage metrics and billing ceilings, helping you track how many rows have been replicated and ensure you stay within your plan's limits.