Model Context Protocol (MCP) Tutorial: Build a Text-to-SQL MCP Server & AI Agents

Analytics Vidhya · Beginner ·🤖 AI Agents & Automation ·2mo ago

Skills: Agent Foundations90%

Key Takeaways

Builds a Text-to-SQL Model Context Protocol server and AI agents from scratch

Original Description

AI agents are everywhere, but how do they actually talk to your databases, Notion, or GitHub? Meet the Model Context Protocol (MCP)—the "USB-C for AI" that is revolutionizing how we connect LLMs to external data. Code Link - https://github.com/sjsoumil/IPL-Text2SQL-MCP In this MCP server tutorial, we move past the theory and build a real-world mcp server and ai agents project from scratch. We are building a Text-to-SQL MCP server on top of an IPL Cricket Database (2008-2026). You'll learn how to ask questions in natural language and have an AI agent generate, validate, and execute SQL queries automatically. Chapters- 0:00 - Introduction: The Glue for AI Agents 1:08 - The "N x M" Problem: Why traditional integration fails 2:24 - What is Model Context Protocol (MCP)? 3:08 - Architecture: MCP Host, Client, and Server 4:00 - Understanding Tools, Resources, and Prompts 4:42 - Stdio vs. HTTP Transport Modes 5:00 - MCP vs. Function Calling: What's the difference? 5:37 - Project Overview: IPL Cricket Text-to-SQL (2008-2026) 6:53 - Environment Setup & Dependencies 7:28 - Step 1: Building the Text-to-SQL MCP Server 9:49 - Step 2: Creating the React Agent Client (LangGraph) 10:32 - Defining System Instructions & Agent Logic 12:05 - Live Demo: Running the Server & Querying the DB 13:05 - Why MCP makes AI tools reusable and scalable 14:12 - Wrap Up & Final Recap

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Chapters (15)

Introduction: The Glue for AI Agents

1:08 The "N x M" Problem: Why traditional integration fails

2:24 What is Model Context Protocol (MCP)?

3:08 Architecture: MCP Host, Client, and Server

4:00 Understanding Tools, Resources, and Prompts

4:42 Stdio vs. HTTP Transport Modes

5:00 MCP vs. Function Calling: What's the difference?

5:37 Project Overview: IPL Cricket Text-to-SQL (2008-2026)

6:53 Environment Setup & Dependencies

7:28 Step 1: Building the Text-to-SQL MCP Server

9:49 Step 2: Creating the React Agent Client (LangGraph)

10:32 Defining System Instructions & Agent Logic

12:05 Live Demo: Running the Server & Querying the DB

13:05 Why MCP makes AI tools reusable and scalable

14:12 Wrap Up & Final Recap

AI Agents Are Starting to Talk to Each Other... Without Us.