✅ Learn how to build robust and scalable software architecture: https://arjan.codes/checklist. Want your AI tools to actually *do* something? In this video, I’ll show you how to integrate external tools with language models using **MCP (Model Context Protocol)**. You’ll learn two common architecture patterns, see real code examples, and get tips on keeping your setup clean and scalable. Whether you’re building for Claude, ChatGPT, or any other LLM—this is how you connect your backend to AI. 🔥 GitHub Repository: https://git.arjan.codes/2025/mcp-server. 🎓 ArjanCodes Courses: https://www.arjancodes.com/courses. 🔖 Chapters: 0:00 Intro 0:46 What is MCP? 3:14 YouTube MCP Version 1 7:58 YouTube MCP Version 2 12:18 Final Thoughts #arjancodes #softwaredesign #python

Get the full code: https://github.com/riza-io/examples/tree/main/demos/mcp_and_pydanticai Try out Riza's MCP server: https://docs.riza.io/getting-started/mcp-servers PydanticAI is one of the most popular frameworks for building AI agents in Python, and it recently launched MCP support. If you've been wanting to learn MCP, this is a great place to start. In this tutorial you'll build a simple agent with PydanticAI, and then add an MCP server called fetch, which enables web browsing. Then we'll use the Postgres MCP server to add database querying to our agent. Then we'll use Riza's remote MCP server to add a code interpreter to our agent.

In this comprehensive guide, the author discusses MCP, it's components, and takes a deep dive on how to build servers from scratch.