✨ Introduction: From Master Control Program to Model Control Protocol

In the original Tron, the Master Control Program (MCP) was an artificial intelligence that didn’t just process data, it made decisions, controlled access to systems, and dictated outcomes. In 2025, MCP has taken on a new meaning: Model Context Protocol.

While the acronym is the same, the intent couldn’t be more different. This MCP isn’t about restricting access – it’s about unlocking it.

If you’ve seen demos of language models using tools or calling APIs, you’ve likely been shown how an AI can fetch the weather or reverse a string. That’s cute, but it’s not useful. It’s a novelty.

This post isn’t a technical how-to. It’s not a feature announcement. It’s a reality check for product owners and stakeholders who want to understand what this technology actually means for them.

🤔 What Is MCP, Really?

Model Context Protocol is a standard that allows large language models (LLMs) to:

• Discover what capabilities your system offers
• Call those capabilities securely and in context
• Act on behalf of a user, not just respond to prompts

Think of it as Alexa Skills for LLMs – but instead of voice commands, the model uses structured reasoning to invoke tools like get_customer_orders, fetch_legal_terms, or save_draft_invoice.

The value of MCP isn’t that it lets a model talk to your app; it’s that it lets the model do things within your app.

📅 From Instructions to Capabilities: The Agentic Shift

Most of what we think of today as “AI integration” is really just automation with a smarter step or two. We write detailed instructions — fetch this data, run this model, format that output — and somewhere in the middle, we invoke an LLM to generate a summary or suggest a classification. It’s still us in control, the LLM is just a function in the workflow.

But Model Context Protocol (MCP) flips that model.

Instead of giving the system instructions, you give the model capabilities.

You say:

“Here are the tools you’re allowed to use. Here’s the user’s intent. Figure out how to solve the problem.”

This isn’t a technical discussion, but we do need to touch on a couple of aspects. MCP consists of different components, including (among others) servers and clients. You can define a server, which exposes tools. Tools are specific capabilities, either to do something, or retrieve specific information.

With these capabilities, the model becomes the orchestrator; you provide the boundaries, not the blueprint.

📘 A practical example: medical insights

Traditionally, if a patient asks for post-op recovery risks for ACL reconstruction based on personal history and internal case data, your system would need:

• Code to fetch EMR records
• A clinical outcomes database
• Logic to compare demographic data
• A narrative template for the summary

But with MCP, you can create a server to expose tools like:

• get_patient_history
• get_population_outcomes
• get_internal_statistics
• generate_summary_report

That last one is a little contrived and just for illustration – the model infers that generating the summary report is its task. More likely, if you have specific data visualisation tools, you would expose these instead, allowing the model to utilise them in the report it generates.

The model can now reason over the task and decide which tools to use, in what order, to generate the best possible answer. Instead of executing a predetermined pipeline or workflow, the model itself decides how to solve the problem using the tools at its disposal.

✉️ Another example: customer support triage

Think about a helpdesk inbox. If you wanted to automate it to reduce wait times and dependence on availability of human agents, traditionally, you’d write:

• Rules to detect topic
• Logic to classify severity
• Escalation paths
• Templates for responses

With MCP, you let the LLM triage and act:

• search_knowledge_base
• send_response
• create_ticket
• escalate_ticket

Even better, you can expose tools to automatically handle certain operations. Now, your model isn’t just a glorified traffic warden, it can actually resolve requests automatically, respond to requesters, add a ticket to a queue if it can’t resolve it, and automatically detect when something is urgent enough to escalate to a human agent immediately.

The result is tickets processed autonomously — fast, fairly, and with transparency — all using tools you control.

🧠 What changes with MCP?

With traditional AI automation:

• You own the flow
• The model fills a step

With MCP:

• The model owns the reasoning
• You own the capabilities

You’re not writing process flows, you’re exposing a system of verbs, and giving the model permission to act.

It’s less like writing a recipe, and more like handing your sous-chef a stocked kitchen and asking for the best dish they can prepare for your guest; or better yet, for all your guests based on each of their individual dietary requirements.

🔧 Why This Matters for Product Teams

Is MCP the latest carriage on the AI hype train? Well, yes. But as we’ve seen in the hypothetical scenarios, it opens some real possibilities; not just for automating known workflows, but automating the creation of ad-hoc workflows as required.

It’s about changing how your system exposes value to intelligent agents – models that reason, act, and learn on the fly.

With MCP:

• You expose capabilities securely, as tools
• The model can invoke them without brittle prompt engineering
• You stop writing orchestration glue and start publishing contracts for what your system can do
• You future-proof your platform for LLMs that can solve real user problems

You don’t need write a chatbot – you need to write the interface your chatbot, or any AI, can use. And, perhaps most importantly, not just to automate solving problems you already know how to solve, but instead giving them the capability to solve problems you may not have even thought of yet.

✅ What You Should Take Away

MCP turns language models into more than just text generators. It lets them act.

It gives you a way to offer your system’s capabilities to models as tools, and lets those models decide how to use them to reach a goal.

You don’t need a new UI. You don’t need to refactor your entire product. Instead, you make your capabilities available to AI agents. You just need to identify the parts of your system a smart agent could use to solve real problems.

That’s what MCP is.

If you want to explore the risks and opportunities that MCP offers, we’re here to help.

(End program.)