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If you’ve been curious about how to build your own AI agents that can talk to APIs, connect with tools like databases, or even follow documentation you’re in the right place.
Microsoft has created something called MCP, which stands for Model‑Context‑Protocol. And to help you learn it step by step, they’ve made an amazing MCP Resources Hub on GitHub.
In this blog, I’ll Walk you through what MCP is, why it matters, and how to use this hub to get started, even if you’re new to AI development.
What is MCP (Model‑Context‑Protocol)?
Think of MCP like a communication bridge between your AI model and the outside world. Normally, when we chat with AI (like ChatGPT), it only knows what’s in its training data. But with MCP, you can give your AI real-time context from:
- APIs
- Documents
- Databases
- Websites
This makes your AI agent smarter and more useful just like a real developer who looks up things online, checks documentation, and queries databases.
What’s Inside the MCP Resources Hub?
The MCP Resources Hub is a collection of everything you need to learn MCP:
- Videos
- Blogs
- Code examples
Here are some beginner-friendly videos that explain MCP:
|
Title |
What You’ll Learn |
|
See how VS Code and MCP build an app with AI connecting to a database and following docs. |
|
|
Learn how MCP makes GitHub Copilot smarter with real-time tools. |
|
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Host your own MCP servers using Azure in C#, .NET, or TypeScript. |
|
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See how to use APIs as tools inside your AI agent. |
|
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Create a chat app powered by MCP in .NET Aspire |
Tip: Start with the VS Code videos if you’re just beginning.
Blogs Deep Dives and How-To Guides
Microsoft has also written blogs that explain MCP concepts in detail. Some of the best ones include:
- Build AI agent tools using remote MCP with Azure Functions: Learn how to deploy MCP servers remotely using Azure.
- Create an MCP Server with Azure AI Agent Service : Enables Developers to create an agent with Azure AI Agent Service and uses the model context protocol (MCP) for consumption of the agents in compatible clients (VS Code, Cursor, Claude Desktop).
- Vibe coding with GitHub Copilot: Agent mode and MCP support: MCP allows you to equip agent mode with the context and capabilities it needs to help you, like a USB port for intelligence. When you enter a chat prompt in agent mode within VS Code, the model can use different tools to handle tasks like understanding database schema or querying the web.
- Enhancing AI Integrations with MCP and Azure API Management Enhance AI integrations using MCP and Azure API Management
- Understanding and Mitigating Security Risks in MCP Implementations Overview of security risks and mitigation strategies for MCP implementations
- Protecting Against Indirect Injection Attacks in MCP Strategies to prevent indirect injection attacks in MCP implementations
- Microsoft Copilot Studio MCP Announcement of the Microsoft Copilot Studio MCP lab
Code Repositories Try it Yourself
Want to build something with MCP? Microsoft has shared open-source sample code in Python, .NET, and TypeScript:
|
Repo Name |
Language |
Description |
|
Python |
Recommended for Secure remote hosting Sample Python Azure Functions demonstrating remote MCP integration with Azure API Management |
|
|
Python |
Sample Python Azure Functions demonstrating remote MCP integration |
|
|
C# |
Sample .NET Azure Functions demonstrating remote MCP integration |
|
|
TypeScript |
Sample TypeScript Azure Functions demonstrating remote MCP integration |
|
|
TypeScript |
Microsoft Copilot Studio MCP lab |
You can clone the repo, open it in VS Code, and follow the instructions to run your own MCP server.
Using MCP with the AI Toolkit in Visual Studio Code
To make your MCP journey even easier, Microsoft provides the AI Toolkit for Visual Studio Code. This toolkit includes:
- A built-in model catalog
- Tools to help you deploy and run models locally
- Seamless integration with MCP agent tools
You can install the AI Toolkit extension from the Visual Studio Code Marketplace. Once installed, it helps you:
- Discover and select models quickly
- Connect those models to MCP agents
- Develop and test AI workflows locally before deploying to the cloud
You can explore the full documentation here: Overview of the AI Toolkit for Visual Studio Code – Microsoft Learn
This is perfect for developers who want to test things on their own system without needing a cloud setup right away.
Why Should You Care About MCP?
Because MCP:
- Makes your AI tools more powerful by giving them real-time knowledge
- Works with GitHub Copilot, Azure, and VS Code tools you may already use
- Is open-source and beginner-friendly with lots of tutorials and sample code
It’s the future of AI development connecting models to the real world.
Final Thoughts
If you’re learning AI or building software agents, don’t miss this valuable MCP Resources Hub. It’s like a starter kit for building smart, connected agents with Microsoft tools.
Try one video or repo today. Experiment. Learn by doing.