In this guide, we will cover how to use superglue MCP to build custom database query workflows in Cursor. Working with databases often involves complex multi-table joins, aggregations, and data transformations. Superglue MCP can automate these processes, enabling the creation of sophisticated database queries through natural language prompts. This guide demonstrates how to build and run a workflow that:
  1. Connects to a PostgreSQL database containing LEGO dataset
  2. Executes complex queries across multiple tables
  3. Returns structured data for analysis and reporting
You can use the superglue client SDK to do this, but in this tutorial we will cover how to build this workflow using superglue MCP.
If you want to use this setup to query your Supabase DB: You may need to enable IPv4 support in your database settings. Other than that, Supabase works just like any other PostgreSQL database with superglue MCP. Get your Supabase connection string by clicking Connect on your Supabase dashboard.

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Prerequisites

  • Ensure that you have added superglue MCP to your mcp.json
mcp.json
{
	"mcpServers": {
	  "superglue": {
			"command": "npx",
			"args": [
				"mcp-remote",
				"https://mcp.superglue.ai",
				"--header",
				"Authorization:${AUTH_HEADER}"
			],
			"env": {
				"AUTH_HEADER": "Bearer YOUR_SUPERGLUE_API_KEY"
			}	
	    }
	}
}
Make sure to replace the API key placeholder with your own API key after copying.

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Building a Custom Database Query Workflow

You can find detailed descriptions of all available tools provided by superglue MCP here. In this tutorial, we will build a custom database integration workflow using natural language through your Cursor chat interface. Here’s how to create a workflow that analyzes LEGO data:

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Example Prompts:

Find the most popular LEGO themes by number of sets
Get detailed information about parts and colors for specific sets
Calculate inventory statistics across different themes

Always tell superglue your database connection: postgres://superglue:superglue@database-1.c01e6ms2cdvl.us-east-1.rds.amazonaws.com:5432/lego

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What Happened Under the Hood:

  • superglue MCP used superglue_create_integration to create a database integration with the provided connection string
  • superglue MCP used superglue_build_and_run to build and execute a workflow based on your natural language request
  • The workflow was created, executed, and returned results
  • Optionally, the workflow can be saved using superglue_save_workflow for future reuse

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Example: Creating a Persistent Database Query Workflow

The database used in this example is readonly. If you want to create your own writable database for testing, you can set up a local PostgreSQL instance:
# Start PostgreSQL Container
docker run --name lego-postgres -e POSTGRES_PASSWORD=password -e POSTGRES_DB=lego -p 5432:5432 -d postgres:15

# Download and load the dataset
wget https://raw.githubusercontent.com/neondatabase/postgres-sample-dbs/main/lego.sql
psql -d "postgres://postgres:password@localhost:5432/lego" -f lego.sql
Let’s say you want to create a reusable workflow for analyzing LEGO sets by theme: 
Build a workflow that:
1. Takes a theme name as input
2. Returns all sets in that theme with their piece counts
3. Calculates the average piece count for the theme
4. Lists the top 5 largest sets in the theme

Database connection: postgres://postgres:password@localhost:5432/lego
After building and testing with superglue_build_and_run, you can save it for future use:
  • The workflow will be saved with a descriptive ID
  • You can execute it anytime using superglue_execute_workflow with different theme names
  • Generate integration code using superglue_get_workflow_integration_code

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Next Steps

  • Reuse Workflows: Execute your database workflows anytime using superglue_execute_workflow with the workflow ID, or programmatically using the generated integration code
  • Complex Analytics: Build workflows for advanced analytics, data mining, or reporting across multiple database tables
  • Multi-Database: Create workflows that join data across different databases or combine database queries with API calls
  • Real-time Queries: Set up workflows for live database monitoring and alerting