MCP

Introduction Nowadays, much of the online commentary related to Generative AI is focussed on Automated code Generation and rightly so. But Lets look at alternate use case of it. Traditionally, Business Intelligence (BI) relies on custom-built dashboards and reports that query a data warehouse. This often requires heavy intervention from engineering teams to analyze requirements, develop artifacts, and maintain them over time. Imagine your data warehouse is built on a SQL-based DBMS with a custom web interface for analytics. Even with this setup, typical challenges remain: ...

Introduction This article compiles my notes on an excellent architectural deep-dive by Jeffrey Richter. He outlines how to architect robust AI Agents using Google’s Agent-to-Agent (A2A) protocol for coordination and Anthropic’s Model Context Protocol (MCP) for tool integration. It is a must-read for anyone moving beyond simple chatbots to complex agentic workflows. You can read the original article here. ...

Introduction In the ever-evolving landscape of AI and machine learning, Google’s MCP Toolbox for Databases stands out. This open-source server enables developers to connect generative AI applications to enterprise databases, facilitating prompt-based querying and natural language processing (NLP). Whether you’re setting up your LLM on-premises using OLLAMA or leveraging providers like Gemini, Claude, or OpenAI, this toolbox offers a versatile and powerful solution. Lets explore it in detail. ...