Model Context Protocol (MCP) Explained: The Open Standard Reshaping AI Development

If you have been following the AI tooling space closely, you have probably heard "MCP" mentioned more and more over the past year. It started as an Anthropic project in late 2024. By mid-2025 it had become an industry standard that OpenAI, Google DeepMind, Microsoft, and Salesforce all adopted. By early 2026 it was donated to the Linux Foundation and tens of thousands of MCP servers existed in the wild. And yet most developers still do not have a clean mental model of what MCP actually is. The explanations tend to be either too abstract ("it is a protocol for connecting AI to tools") or too technical to be immediately actionable. This post tries to fix that. The Problem Before MCP To understand why MCP matters, you need to feel the pain it solves. Before MCP, every AI tool integration was a bespoke implementation. If you wanted Claude to read your GitHub issues and create a Jira ticket, someone had to write code that: Authenticated with GitHub's API Fetched the issues Formatted them in