Model-Context-Protocol

Introduction I’ve been doing a lot of research on AI agents recently, exploring different frameworks, architectures, and models. The speed of innovation is incredible, especially the recently released Large Language Models (LLMs) Claude Opus 4.6 and Claude Sonnet 4.6, and OpenAI’s GPT 5.3 Codex. What I’ve found is that it really helps to try different models and see which one fits your use case best. For example, Claude Opus 4.6 and GPT 5.3 Codex really take the time to understand the codebase before making changes when you use them in agent mode, they analyze the full context, read related files, and plan their edits rather than jumping straight to code generation. However, one good thing I find very helpful is to make multiple iterations with different models and compare the results side by side to see which one is better for your specific use case. This also gives you more insight into how different models approach the same problem, which can be very valuable when you’re building for example an AI agent. It’s also a learning process. ...

Introduction I’ve been experimenting with the Model Context Protocol (MCP) for a while now, and it’s changed how I think about extending AI assistants like GitHub Copilot. Then, Microsoft recently released a stable version of the new MCP trigger for Azure Functions. It made building a cloud-native, serverless MCP server incredibly straightforward. As soon as I saw it, I had an idea: what if I could use this to build a simple, cloud-based tool to save and retrieve notes directly from Copilot? ...