This course provides a complete, hands-on understanding of the Model Context Protocol (MCP) — the emerging standard that connects AI models with external data, tools, and environments. You’ll learn not just what MCP is, but why it matters in the evolving AI ecosystem. Starting with the fundamentals, the course explains the core concepts and architecture behind MCP and walks you through the entire lifecycle of the protocol. You’ll then get practical experience by building and connecting MCP servers, integrating them with tools like Claude Desktop, and deploying both local and remote servers. By the end, you’ll have the knowledge to design, implement, and deploy your own MCP integrations, empowering AI systems to reason, retrieve, and act intelligently within real-world contexts. What you will learn: What the Model Context Protocol is and why it matters MCP architecture and lifecycle explained Connecting MCP servers to external clients (like Claude Desktop) Building local MCP servers from scratch Deploying and managing remote MCP servers
Curated by: CampusX (8 videos)
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/mcp-client Notes: https://learnwith.campusx.in/s/store/courses/YouTube%20Notes Quick Revision: https://docs.google.com/document/d/1I26xHGosv0B7zgDl0sM_GZtlX20rPWYBPZHEcoREN7A/edit?usp=sharing Learn LangGraph: https://www.youtube.com/playlist?list=PLKnIA16_RmvYsvB8qkUQuJmJNuiCUJFPL Config Part: https://youtu.be/y-uPv3ltOTY?t=915&si=ObvzsrnWJZjDkG33 Async/Await Tutorial: https://realpython.com/async-io-python/ ============================ Did you like my teaching style? Check my affordable mentorship program at : https://learnwith.campusx.in DSMP FAQ: https://docs.google.com/document/d/1OsMe9jGHoZS67FH8TdIzcUaDWuu5RAbCbBKk2cNq6Dk/edit#heading=h.gvv0r2jo3vjw ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at support@campusx.in ⌚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