Technology Explorations in Data & AI

In this conversation, Jonny Daenen and Pierre Crochelet explore the Model Context Protocol (MCP), a framework that enhances AI assistants by allowing them to perform various tasks through tools, resources, and prompts. 

They discuss the architecture of MCP, how to build an MCP server, and the developer flow for creating tools. The conversation also touches on the compatibility of MCP with different AI agents and the user experience, highlighting both the potential and limitations of the protocol.

Resources:
  • Demo code: https://github.com/datamindedbe/demo-technology-exploration/tree/main/demos/claude_mcp
  • MCP servers: https://github.com/modelcontextprotocol/servers
  • MCP directory: https://mcp.so/
  • Click here to watch a video of this episode.
  • Full playlist: https://www.youtube.com/playlist?list=PLJ_da7qdfL80rA7byzC_CmyrfJWjcCTnb

Chapters:
  • (00:00) - Introduction
  • (01:03) - Demo: Claude Desktop & MCP
  • (04:43) - What is the Model Context Protocol?
  • (07:09) - Tools, Resources & Prompts
  • (08:20) - The protocol: Host-Client-Server
  • (11:05) - Building your own MCP server
  • (16:46) - Prompts, resources & tool functionality
  • (19:45) - Developer flow & the Inspector
  • (23:31) - Function limitations & return types
  • (26:34) - Testing the tool & integrating other agents
  • (28:46) - Community MCP servers & current limitations
  • (32:11) - Summary & next steps


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

Creators and Guests

Host
Jonny Daenen
Head of Knowledge at Dataminded
Guest
Pierre Crochelet
Data Engineer at Dataminded

What is Technology Explorations in Data & AI?

Deep dives and practical demos on the technologies shaping modern data and AI development. Join the Dataminded team as we explore, unbox, and critically review the latest tools, from building AI agents and RAG systems to optimizing cloud costs and accelerating data pipelines. We cut through the hype to show you what actually works in real data engineering practice, complete with demo code!

00:00 Introduction
01:03 Demo: Claude Desktop & MCP
04:43 What is the Model Context Protocol?
07:09 Tools, Resources & Prompts
08:20 The protocol: Host-Client-Server
11:05 Building your own MCP server
16:46 Prompts, resources & tool functionality
19:45 Developer flow & the Inspector
23:31 Function limitations & return types
26:34 Testing the tool & integrating other agents
28:46 Community MCP servers & current limitations
32:11 Summary & next steps