🚀 Build an AI Agent using OpenAI + MCP + Python | Complete Tutorial in Tamil

In Part 3 of our MCP (Model Context Protocol) series, we move from understanding MCP concepts to building a practical AI Agent using OpenAI, MCP, and Python.

In the previous videos, we learned:

✅ What is MCP?
✅ Why AI Agents need MCP
✅ MCP Architecture
✅ How to build an MCP Server
✅ How to build an MCP Client

Now, we take the next step...

👉 We build an AI Agent that can discover MCP Tools, understand the user's request, select the appropriate tool, execute it through MCP, and use the result to generate the final answer.

🔥 WHAT YOU WILL LEARN

⏱️ Video Timeline

00:00 – Introduction
[Add your actual timestamps after YouTube processing]

• AI Agent vs Traditional Chatbot
• OpenAI + MCP + Python Architecture
• Connecting an AI Agent to an MCP Server
• MCP Tool Discovery
• Converting MCP Tools for OpenAI
• OpenAI Tool Calling
• AI-driven Tool Selection
• MCP Client Tool Execution
• Returning Tool Results to the LLM
• Generating the Final AI Response
• Understanding the Complete Agentic AI Flow
• Why MCP is Powerful for Real-World AI Agents

🧠 TOOLS USED

• Python
• OpenAI API
• MCP (Model Context Protocol)
• MCP Python SDK
• MCP Server
• MCP Client

💡 THE COMPLETE FLOW

User
↓
AI Agent
↓
OpenAI LLM
↓
Tool Selection
↓
MCP Client
↓
MCP Server
↓
MCP Tool
↓
Tool Result
↓
OpenAI LLM
↓
Final Answer

This is where MCP becomes really interesting for Agentic AI.

MCP provides a structured way for AI Agents to discover and use external tools, making it easier to build powerful real-world AI applications.

🔗 LEARN MORE

Visit SomethingTalk1:

Teltam.in

📺 Watch the MCP Series

Part 1: What is MCP?

Part 2: Build MCP Server & Client

Part 3: Build AI Agent using OpenAI + MCP + Python

🔔 Subscribe for More Videos

Learn practical concepts in:

• Agentic AI
• MCP
• Generative AI
• LLMs
• LangChain
• LangGraph
• Python
• AI Engineering
• AI Tools

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