MCP: Understanding it like i am 5
I just built an AI agent after a week of research and thought I’d share my two cents on MCP. Please don’t crucify me if I get something wrong — I’m still learning this tool that’s supposedly coming for our jobs… or so they say! 🤣
Think of the Model Context Protocol (MCP) as a smart power strip for AI integrations — like a travel adapter that lets you plug in any device, no matter the country. Instead of needing a different converter for each outlet, you have a single, universal connection point. MCP works the same way: it’s an open standard that enables AI models to communicate with various applications and data sources in a unified manner. Rather than requiring custom integrations for every tool, MCP provides a standardized interface, making AI integration more fluid and efficient.
What does this mean in practical terms? If you use an AI coding assistant like Cursor, MCP acts as the bridge that allows these assistants to interact with external tools on your behalf. With MCP, an AI model can retrieve data from a database, modify a design in Figma, or control a music app — all using natural language instructions through a common framework. This eliminates the need to manually switch between different tools or learn each one’s API, as MCP streamlines communication between human commands and software commands.
In essence, MCP functions like a universal adapter, connecting all digital services. Any software that supports MCP can instantly integrate with AI models, removing the need for customized connectors for each new tool. The result? AI assistants become far more capable — not just answering questions but performing real-world tasks across multiple applications.
2. AI: From Text Prediction to Actionable Agents
Early large language models (LLMs) functioned primarily as text predictors — you provide an input, and they generate text based on patterns learned from training data. While effective for answering questions and drafting content, these models were isolated from real-time data and external tools. For example, if you asked a 2020-era AI to check your email or retrieve a file from a database, it wouldn’t work because it lacked the ability to interact beyond text generation.
3. The Problem MCP Solves
This realization led Anthropic, the company behind the Claude AI assistant, to introduce MCP in late 2024. As AI capabilities grew, the biggest challenge became connectivity rather than intelligence. Every new data source or tool required custom integration, slowing down progress. MCP was created to address this problem, much like how HTTP revolutionized the internet by standardizing communication between web pages. MCP represents the next step in AI evolution — shifting from text-based responses to AI agents with a universal tool interface.
Without MCP, integrating AI with external tools is like dealing with appliances that each require a unique power adapter — messy, inefficient, and unsustainable. Developers faced constant hurdles with fragmented integrations. For example, an AI assistant might use one approach to retrieve tweets, another to query a database, and yet another to automate design edits. Each integration demanded custom development, making the process labor-intensive and difficult to scale.
Another major challenge MCP addresses is the inconsistency in tool communication. Each software application or service has its own API, data structure, and syntax, creating a “language barrier” for AI agents. For example, fetching a report from your mail, querying a SQL database, and editing a figma file all involve distinct processes in a pre-MCP world.
In Summary
MCP is a game-changer for AI integrations, allowing AI to function as a true digital assistant rather than a standalone chatbot. With MCP, AI assistants can finally operate across different platforms as effortlessly as plugging a device into a universal adapter.
Build an AI agent today! 🤣
