How to build MCP Clients | MCP Trilogy | CampusX

In this video, we learn how to build your own MCP client and connect it to multiple MCP servers — including a local Math server, a remote Expense Tracker server, and even a Manim animation server. We go step-by-step through the setup, tool-calls, and finally create a clean Streamlit-based chatbot UI that can talk to all these servers. Perfect for anyone learning MCP, LangChain, or custom AI tool integrations.

Code: https://github.com/campusx-official/m...
Quick Revision: https://docs.google.com/document/d/1I...
Learn LangGraph:    • Agentic AI using LangGraph  
Config Part:    • Model Context Protocol | The How | How to ...  
Async/Await Tutorial: https://realpython.com/async-io-python/

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DSMP FAQ: https://docs.google.com/document/d/1O...
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⌚Chapters⌚

00:00 — Intro & playlist recap (apology + what we covered so far)
00:28 — MCP playlist structure & approach (WHY / WHAT / HOW)
03:54 — Video goal & servers overview (what we’ll build — Math + Expense servers)
06:13 — Local Math MCP server demo & testing (uvicorn, inspector)
08:07 — Project setup & dependencies (folder, uv init, install libs)
09:23 — Build client: skeleton, async structure & server config
14:06 — Tools & LLM binding (fetch tools, name-tools dict, bind OpenAI LLM)
19:18 — Tool-call flow & invocation (extract tool calls, invoke tools, get results)
23:32 — Returning tool results to LLM & final response (ToolMessage + history)
29:29 — Multi-server support (loop handling + add remote Expense Tracker)
33:37 — External servers & Manim animation integration (generate animations)
35:21 — Streamlit GUI: convert logic to UI and live demo
39:25 — Summary, next steps (LangGraph playlist) & outro