In the ever-expanding universe of AI and LLMs, there’s a “buzz” acronym going around that may just save your team from chaos, inconsistency, and the dreaded “AI debt.” It’s called Model Context Protocol (MCP), and no, it’s not trying to take over the Grid like its namesake from Tron. (Though it might be just as powerful in the right hands.)
The Challenge of AI Implementation
If you’ve been dabbling with LLMs like ChatGPT, Claude, or any of their rapidly multiplying cousins, you’ve probably noticed the frustrating inconsistency in the output you get using prompts. One day your AI assistant writes perfect user stories, the next day it’s spinning tales about features you never asked for.
Why does this happen? Most people are just asking their AI models to perform tasks or output a variety of data, without providing proper context or assembly instructions.
Enter the Model Context Protocol
At its core, a Model Context Protocol (MCP) is a structured framework for managing what information (ingredients) you feed to AI models, when you feed it, and how you format it (the assembly instructions). Think of it as the recipe book for an AI sandwich-making robot.
Without an MCP, you’re essentially walking into a kitchen, dropping a bunch of random ingredients on the counter, shouting “Make me a sandwich!” and then being surprised when the results are inconsistent. With an MCP, you’re providing a well-organized mise en place and a detailed recipe card.
The Sandwich-Making Robot Analogy
Let me explain MCPs with a simple analogy: Imagine you have a sandwich-making robot (our AI model). You want this robot to make you a BLT sandwich.
If you simply tell the robot, “Make me a sandwich,” without any additional information, what happens? The robot has to guess what kind of sandwich you want. Maybe it makes a peanut butter and jelly. Maybe it makes a grilled cheese. Maybe it piles random ingredients together in a way no human would consider a sandwich. Each time you ask, you might get completely different results.
Why? Because you’ve given the robot (the model) no context (the ingredients) and no protocol (instructions on how to assemble them).
Let’s try again, but better this time:
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First, you provide context: bacon, lettuce, tomato, mayonnaise, and two pieces of toasted white bread.
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Then, you provide a protocol (instructions): “Start with one piece of toasted bread. Spread mayonnaise on that piece. Place a leaf of lettuce on top of the mayonnaise. Add two strips of bacon. Place tomato slices on the bacon. Spread mayonnaise on the second piece of toasted bread and place it on top, mayonnaise side down.”
Now your Sandwich-bot-01 can consistently make a perfect BLT sandwich every time. The robot (model) has the right ingredients (context) and clear instructions on how to use them (protocol).
Anatomy of an Effective MCP
Think of MCP as your AI communication blueprint. A well-designed protocol includes:
System Context: What Your AI Should Always Know
This is the foundation—the knowledge base, rules, and guidelines that define how your AI should behave in all situations. In our sandwich analogy, this is knowing what ingredients are available in the kitchen, how to operate the toaster, and basic food safety rules.
For your product, this is information about features, limitations, brand voice, and any boundaries your AI should respect.
User Context: Personalization That Matters
This is about who is interacting with your AI and what you know about them. In the sandwich world, this is knowing that this particular customer likes extra crispy bacon and light mayo.
For your product or service, this might include the user/customer’s role, preferences, history with your brand, and access level.
Conversation Context: The Flow of Interaction
This captures what’s been discussed so far and what might be relevant for future responses. In sandwich terms, this is remembering that the customer already asked for extra tomatoes and confirmed they want white bread, not wheat.
For your AI assistant, this is maintaining the thread of conversation and remembering what’s already been covered.
Task Context: The Current Goal
What is the user or customer trying to accomplish right now? This focuses the AI on the immediate objective. For our sandwich robot, this is understanding that right now we’re making a BLT, not a club sandwich.
For your product, this might be recognizing if the user is troubleshooting an issue, exploring new features, or trying to complete a specific task.
Common MCP Pitfalls (And How to Avoid Them)
Even with MCPs, there are traps waiting for the unwary product team:
1. Context Overload: More Is Not Always Better
The Problem: Throwing everything including the kitchen sink into your context.
In sandwich terms, this is like giving the robot information about every possible sandwich in existence, the history of bread-making, and detailed profiles of every customer who’s ever ordered a sandwich—when all you need is to make one BLT.
The Solution: Be intentional and selective about what goes into each context. Focus on what’s relevant for the task at hand.
2. Static Context Syndrome: Set It and Forget It
The Problem: Setting context once at the beginning and never updating it.
This is like telling your sandwich robot the customer wants a BLT, but not updating when they request “hold the mayo” halfway through their order.
The Solution: Make your MCP dynamic and responsive to the evolving conversation. Update context as new information becomes available. Just like other business documents, your context should be alive, and evolving.
3. No Optimization Strategy: Running Out of Room
The Problem: No plan for handling context limitations, leading to truncated or irrelevant context.
This is like trying to give the robot instructions for 20 different sandwiches at once, overwhelming its memory so it forgets the critical steps for the BLT it’s actually making.
The Solution: Prioritize the most relevant information and have a strategy for managing context limitations.
The Future of MCPs (or, Where We’re Going, We Need Road Maps to navigate the Grid)
As AI capabilities evolve at warp speed, MCPs will become increasingly sophisticated:
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Standardized Frameworks: Expect to see open-source MCP frameworks emerge with best practices built-in.
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Context Optimization Services: Specialized cloud services that help manage and optimize context for different models.
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Multi-Modal MCPs: Protocols that handle not just text, but images, audio, and even video inputs in structured ways.
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Adaptive Context Engines: Systems that learn which context is most valuable for different types of queries and automatically optimize accordingly.
Getting Started: Your MCP Action Plan
Ready to bring some order to your AI chaos? Here’s a practical roadmap:
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Inventory Your Current AI Touchpoints: Where is AI already used in your product? How consistent are the results?
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Define Your Context Categories: What product knowledge, user data, and task information would make your AI interactions better?
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Start Small: Pick one AI feature and implement a simple MCP for it.
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Measure the Results: Are responses more consistent? Is your team spending less time debugging weird AI behavior?
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Iterate and Expand: Refine your approach based on real usage, then apply it to other AI features.
Conclusion: End of Line (But Just the Beginning for Your AI)
Unlike the power-hungry MCP from Tron, your Model Context Protocol won’t try to take over the world—but it might just save your product team from the growing chaos of unstructured AI implementations.
By bringing intentionality and structure to how your applications communicate with AI models, you’re building a foundation that will make future iterations faster, more reliable, and more maintainable.
As with any product architecture decision, the key is to start simple, learn from real usage, and iterate. Your first MCP doesn’t need to be perfect—it just needs to be better than the “throw ingredients on the counter and hope the robot makes a sandwich” approach that’s all too common today.
So go forth, impose some order on the AI chaos, and may your models always actually understand what your users mean!
About the author: A product designer who has survived enough AI hallucinations to know that structure isn’t just nice to have—it’s how we’ll all keep our sanity in the brave new AI world.