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

Pipe real-time ETL metadata into Cursor. Your agent writes pipeline monitoring scripts using actual live data, not mock variables.

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Connect Hevo Data (ETL & Data Pipeline) MCP to Cursor

Create your Vinkius account to connect Hevo Data (ETL & Data Pipeline) to Cursor 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 Cursor

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Claude

Perplexity

Inject live pipeline IDs into your code

The `list_pipelines` tool drops your active Hevo syncs directly into your editor context. You open Cursor Agent, ask it to write an alerting script, and it pulls the real pipeline IDs to populate the code. No more copying and pasting UUIDs from a browser tab. If you need deeper context on a specific sync, the `get_pipeline` tool fetches the exact configuration. Your AI client reads the schedule, source type, and status, then writes Python requests that match your actual infrastructure. Simple as that.

Cross-reference SQL models locally

Data models define how your raw events turn into usable tables. The `list_models` tool pulls the SQL transformations currently active in your account. You see exactly what happens to the data before it hits the warehouse. When you write dbt models in Cursor, you need to know what Hevo already transformed. This MCP Server lets your agent cross-reference the live ETL models against your local code. You catch schema mismatches before you commit.

Verify destinations and event usage

You have to know where the data goes. The `list_destinations` tool lists your connected Snowflake, BigQuery, or Postgres targets. Your agent uses these exact warehouse names when generating connection strings or Terraform blocks. Before you deploy a script that triggers high-volume syncs, you run `get_usage`. Cursor checks your event quota. You avoid blowing up your monthly billing cycle because the AI knew your exact limits.

Setup guide

Set up Hevo Data (ETL & Data Pipeline) MCP in Cursor

Prerequisites

Cursor installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP Settings

Go to Cursor Settings → MCP or open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and search for "MCP: Add Server".
2

Add the Hevo Data (ETL & Data Pipeline) MCP

Cursor will create or open .cursor/mcp.json in your project root. Paste the JSON snippet on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Enable Agent mode

Open Composer (Cmd+I / Ctrl+I) and switch to Agent mode using the dropdown at the top. MCP tools are only available in Agent mode.
4

Verify the connection

Ask Cursor something like "List my recent Hevo Data (ETL & Data Pipeline) transactions." If the MCP tools are loaded correctly, Cursor will call the Hevo Data (ETL & Data Pipeline) tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

{
  "mcpServers": {
    "hevo-data-etl-data-pipeline-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

Get your connection token →

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Common questions about Hevo Data (ETL & Data Pipeline) MCP in Cursor

Drop the server configuration into your project's .cursor/mcp.json file. Turn on Agent mode in the chat interface.

No. The tools are strictly read-only. You can pull metadata to write code, but you cannot provision new syncs.

Not at all. The tools only execute when you explicitly ask the agent to fetch data. They run as a background process.

You just need API access enabled on your account. If you can generate a token, the tools will work.

It exposes pipeline IDs, destination configurations, and SQL model definitions. It never touches the actual payload data moving through the ETL process.

Use it with your favorite AI tools

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