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Technology Explorations in Data & AI · Nov 3, 2025 · 21 min

Accelerate Data Engineering using MCP Tools

Emil Krause & Jonny Daenen explore how to accelerate dbt development by integrating MCP (Model Context Protocol) with Postgres and Cursor. Emil demonstrates how to solve a database bug by allowing AI agents to interact directly with databases. They discuss the setup of a database MCP server, demonstrate its capabilities in troubleshooting data inconsistencies, and highlight the importance of understanding data even when using advanced tools. The conversation also touches on the potential pitfalls of using such tools and the need for technical expertise in leveraging them effectively. Resources: Demo code: https://github.com/datamindedbe/demo-technology-exploration/tree/main/demos/postgres_mcp MCP 101: https://www.youtube.com/watch?v=fIr55-koOJQ Click here to watch a video of this episode. Full playlist: https://www.youtube.com/playlist?list=PLJ_da7qdfL80rA7byzC_CmyrfJWjcCTnb Creators & Guests Jonny Daenen - Host Emil Krause - Guest Chapters: (00:00) - Introduction: MCP + Postgres (02:20) - Demo: debugging salary percentiles (06:29) - Creating and testing dbt models (07:11) - Benefits and dangers of AI assistance (09:42) - Setting up Postgres MCP in Cursor (12:57) - Challenges & pitfalls (14:54) - MCP vs semantic models (17:16) - Other dev tasks (18:39) - Claude Desktop vs Cursor (19:59) - Summary Data & AI: Technology Explorations is a biweekly show from Dataminded. Each episode a Dataminded engineer demos a tool or technique worth knowing about -- working code, honest takes, no hype. Music by Aleksandr Karabanov from Pixabay

0:00 · Introduction: MCP + Postgres-21:30
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Emil Krause & Jonny Daenen explore how to accelerate dbt development by integrating MCP (Model Context Protocol) with Postgres and Cursor. Emil demonstrates how to solve a database bug by allowing AI agents to interact directly with databases. 

They discuss the setup of a database MCP server, demonstrate its capabilities in troubleshooting data inconsistencies, and highlight the importance of understanding data even when using advanced tools. The conversation also touches on the potential pitfalls of using such tools and the need for technical expertise in leveraging them effectively.

Resources:

  • Demo code: https://github.com/datamindedbe/demo-technology-exploration/tree/main/demos/postgres_mcp
  • MCP 101: https://www.youtube.com/watch?v=fIr55-koOJQ
  • Click here to watch a video of this episode.
  • Full playlist: https://www.youtube.com/playlist?list=PLJ_da7qdfL80rA7byzC_CmyrfJWjcCTnb


Creators & Guests


Chapters:
  • (00:00) - Introduction: MCP + Postgres
  • (02:20) - Demo: debugging salary percentiles
  • (06:29) - Creating and testing dbt models
  • (07:11) - Benefits and dangers of AI assistance
  • (09:42) - Setting up Postgres MCP in Cursor
  • (12:57) - Challenges & pitfalls
  • (14:54) - MCP vs semantic models
  • (17:16) - Other dev tasks
  • (18:39) - Claude Desktop vs Cursor
  • (19:59) - Summary

Data & AI: Technology Explorations is a biweekly show from Dataminded. Each episode a Dataminded engineer demos a tool or technique worth knowing about -- working code, honest takes, no hype.

Music by Aleksandr Karabanov from Pixabay

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