Code Compass

Introduction Model Context Protocol (MCP) is an open standard that makes it easier for applications to provide information to large language models (LLMs). You can think of MCP like a USB-C port for AI —just as USB-C offers a common way to connect devices, MCP provides a standard way to link AI models with different data sources and tools. MCP uses a client-server setup , where different parts work together to share data: MCP Hosts – Programs like Claude Desktop, IDEs, or AI tools that need access to data. MCP Clients – Connect directly to servers to request and send data. MCP Servers – Small programs that provide specific functions through MCP. For this proof of concept (PoC) , I used two key parts of MCP: Resources – A way for MCP servers to share data, which clients can read and use to give better responses. Tools – Functions that let servers do tasks, such as pulling data from other systems or running commands. I built both the MCP server and client inside Cursor , writing all...

Introduction Power BI is a great tool for turning data into clear, interactive reports and the best part?  It works smoothly with Business Central, right out of the box.  You just need to set it up, and you can start viewing powerful reports right inside within Business Central dashboard.  Microsoft provides several ready-made reports, grouped into different apps, so you can pick and install only what you need.  Once set up, these reports help you track key business insights without switching between systems.  In this blog, we’ll walk you through how to set up and use Power BI reports in Business Central to make smarter decisions. References Introduction to Business Central and Power BI Install Power BI apps for Business Central Configuration Open your Business Central and search for "Assisted Setup". Click on "Connect to Power BI" Once the set up page opens, click on Next. Fiscal : A 12-month calendar that begins in any month and ends 12 months after. Standard ...

Introduction Software development has been around since the 1940s. We started with punch cards, then machine language, followed by assembly, high-level programming languages, low code, no code, and now AI-assisted coding. Along the way, several tools have been developed to make programmers' jobs easier, from card sorters and verifiers to debuggers and IDEs. Now, with the advent of AI, we have large language models (LLMs) writing code for us, but I don't think it's quite there yet. In this article we'll see how AI assists developers, what it can do for us today, its limitations, and where it's headed. The concept of AI began in the 1950s when researchers tried to imbue machines with the magic to think. Early systems followed set rules, but as computers improved and data became more available, smarter methods emerged, such as machine learning, natural language processing, and neural networks. Large Language Models (LLMs) grew from these advances, using huge amounts of...