Understanding MCP: A New Framework for AI Integration
You’re already familiar with APIs—the building blocks that let software talk to other software. Whether it’s fetching weather data or connecting a frontend to a backend, APIs have quietly powered the digital world for years.
Now, there’s a new concept worth knowing: MCP — Model, Context, Protocol.
It’s a simple way to think about how AI—specifically large language models (LLMs)—can be integrated into your business.
It’s a simple way to think about how AI—specifically large language models (LLMs)—can be integrated into your business.
🧠 Model
This is your AI brain—something like GPT-4 or Claude. But by default, it doesn’t know anything about your business.
📊 Context
To be useful, the model needs context. Asking “Who are my top-performing sales reps?” is meaningless unless the model has access to your sales data.
🔌 Protocol
This is how you connect the model to the context—whether it’s through an API, a database query, or a secured integration.
In traditional software, you'd assign engineers to build APIs that connect frontends to backends, or apps to external services like weather or finance. In the world of AI, we're doing something similar—but with more intelligence and flexibility.
Early attempts to solve this were called RAG (Retrieval-Augmented Generation). This gave AI systems access to your private data in real time—without retraining the model. That shift led to the rapid adoption of vector databases, a new kind of search index optimized for natural language.
Then things got interesting. We began modeling AI agents after real-world job roles. Think:
- Shipping Manager
- Customer Support Agent
- Operations Analyst
Each AI agent has a role, access to tools (like APIs), and responsibilities. It’s like staffing a team—but with software.
Example: An AI “Weatherman” agent might call a weather API and give your logistics team forecasts tailored to their routes. An AI “Driver Support” agent could handle 90% of delivery-related support tickets.
So What’s the Opportunity?
You’re not just using AI to generate text anymore. You’re connecting models to your company’s data and systems—turning them into intelligent agents that can reason, act, and even collaborate with one another.
- Want memory? Cache the data.
- Want privacy? Add authentication.
- Want scale? Agents can run 24/7 with no burnout.
Bottom line:
MCP is a useful lens for understanding how to bring AI into your business. It’s not about replacing people—it’s about equipping your team (and your software) with intelligent collaborators who can accelerate workflows, reduce costs, and unlock insights in real time.
MCP is a useful lens for understanding how to bring AI into your business. It’s not about replacing people—it’s about equipping your team (and your software) with intelligent collaborators who can accelerate workflows, reduce costs, and unlock insights in real time.
Let me know if you'd like help mapping MCP to your business processes.