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

Analytics Vidhya · Beginner ·🤖 AI Agents & Automation ·1w ago

Skills: Agent Foundations90%

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

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