Imagine this: You’re chatting with an advanced AI model like Meta’s LLaMA 4 ( which we discussed in the previous blog). It can summarize reports, write code, and answer complex questions like a professional. But then, it forgets your name within minutes. Ever happened something similar? Think Dory from Finding Nemo smart, cute and loveable but forgets everything almost instantly, right?
Even though LLaMA 4 can handle a massive amount of information at once (up to 10 million tokens!), that’s still short-term memory. It can only hold on to what’s in front of it, not what happened yesterday or even five minutes ago, haha yeah almost human like behaviour. That’s where MCP comes into picture.
MCP gives AI a sort of smart memory. It lets models remember your preferences, pull in information from tools or documents, and keep context across conversations. Think of it as a diary that AI can read from and write to so it doesn’t have to start from scratch every single time.
What Is Memory Context Protocol (Memory Management)
While MCP in AI focuses on managing conversational context, there’s also a Memory Context Protocol concept used in system-level programming especially in environments like PostgreSQL. It is a disciplined way of managing memory using hierarchical memory contexts, which organize allocations into trees of memory blocks. This structure improves memory usage, eases cleanup, and enhances performance in long running or complex systems.
Concept Overview: Hierarchical Memory Management
Traditional systems use malloc and free, where memory is allocated and released manually, or rely on garbage collection, where cleanup is automatic but often inefficient and non-deterministic. Memory context protocols create named memory regions — called contexts — where memory is allocated in bulk. These contexts can be nested within each other, allowing bulk cleanup by simply resetting or deleting the parent.
This technique provides greater control and predictability over memory usage, helping developers prevent memory leaks, reduce complexity, and optimize execution paths.
Lets try with a Simple Analogy:
If AI is a brain, then MCP is like the notebook it carries around to jot things down. This notebook helps it remember names, tasks, and preferences so it can respond better the next time you talk.
It’s what bridges the gap between a one-time interaction and a long-term intelligent assistant.
Why Do AI Models Like LLaMA 4 Need MCP?
Even the smartest models have a few limitations:
1. They forget easily
They only “see” what’s in their context window. That means once the conversation gets too long, earlier messages get pushed out and forgotten.
2. They don’t remember past chats
Each new session is a blank slate. You have to re-explain who you are, what you like, and what you’re working on every single time.
3. They can’t fetch live data
Out of the box, AI models can’t access your Google Calendar or company database. Without help, they’re limited to what they were trained on.
4. They don’t personalize unless guided
Without memory, personalization has to be manually prompted every time. With MCP, personalization becomes built-in.
MCP solves all of these by giving the model tools to remember context, access relevant data, and personalize responses just like a human assistant would.
If you’re building AI solutions and want them to behave more contextually, we offer AI consulting services to help you architect intelligent systems using concepts like MCP. Whether you’re looking to improve user experience, personalize outputs, or enhance multi-turn memory, our Generative AI integration solutions can turn ideas into production-ready applications.
How MCP Works
MCP organizes the way AI remembers, fetches, and applies relevant context. This is how it works
1. Session Memory
This is like short-term memory the recent chat history. MCP ensures the AI can reference what you just said. It allows smoother multi-turn conversations without losing track.
2. Long-Term Memory
Important information like user preferences, project status, or historical data gets stored outside the model. This lets the AI remember across days, weeks, or months.
3. Retrieval System (RAGs)
Rather than guessing or relying on limited context, MCP allows the AI to retrieve info — like a smart search engine — from vector databases or structured documents.
4. Context Window Management
AI models can only read a limited amount of content at once. MCP decides what matters most, compresses past conversation where needed, and keeps the most useful parts visible.
5. Instruction Persistence
Tell the AI once to be casual or avoid certain topics MCP ensures it remembers that behaviour across chats, not just for the current session.
A Quick Look: AI With vs. Without MCP
| Feature | Without MCP | With MCP |
| Remembers previous chats | ❌ | ✅ |
| Accesses live/external data | ❌ | ✅ |
| Learns your preferences | ❌ | ✅ |
| Personalized responses | ❌ | ✅ |
| Efficient, focused replies | ❌ | ✅ |
These differences aren’t just technical — they impact user trust, productivity, and long-term usability. If you’re planning to embed AI into your customer service, apps, or internal tools, our team can assist with generative AI integration, ensuring your system is both smart and memory-aware.
ChatGPT (OpenAI)
ChatGPT now supports memory features as part of OpenAI’s latest updates. You can set custom instructions, and the AI will remember things about you over time.
Meta AI in WhatsApp
Meta is integrating MCP-like context awareness into their chat tools like Messenger and WhatsApp. Expect smarter replies and less repetition.
Microsoft Copilot
Copilot in Office apps uses organizational context, recent activity, and user-specific preferences a practical use of MCP concepts to boost productivity.
The MCP Loop (How It Flows)
Here’s the step-by-step cycle:
- Capture – You ask something.
- Embed & Store – AI pulls key info and saves it.
- Retrieve – Later, when needed, AI searches that memory.
- Inject – Adds the retrieved info to the current conversation.
- Respond – Gives you a smarter, more relevant answer.
Each interaction builds a smarter and more personalized experience.
Benefits of Using MCP in Systems
- Avoids memory leaks by eliminating the need to track individual allocations.
- Simplifies memory management, especially in nested or stateful systems.
- Improves code readability and maintainability by linking memory usage to logical operations.
- Boosts performance in high-throughput or long-running processes where repeated allocations and deallocations would be costly.
Implementation Example
Here’s a basic sketch of how memory contexts can be implemented in C:
MemoryContext context = MemoryContextCreate("MyContext");
void* ptr = MemoryContextAlloc(context, sizeof(MyStruct));
// ... use memory
MemoryContextReset(context); // or MemoryContextDelete(context);This approach is widely used in PostgreSQL. During query execution, separate contexts are created for each query plan or expression node. Once the query completes, all memory can be released instantly by resetting the context.
- PostgreSQL relies on memory contexts as the foundation for managing memory throughout the query lifecycle.
- Embedded Systems – Limited memory environments benefit from grouped allocation and cleanup.
- Game Engines – Ideal for managing temporary memory for rendering frames, game physics, or AI routines.
- High-Frequency Trading Platforms – Where deterministic memory handling is crucial for consistent low-latency performance.
Common Pitfalls and Best Practices
Common Mistakes
- Overusing nested contexts unnecessarily, leading to bloated trees.
- Forgetting to reset or delete contexts after use, resulting in leaks.
- Mixing standard malloc/free logic with memory context logic, which can cause inconsistencies or double frees.
Best Practices
- Keep context hierarchies shallow and purposeful.
- Use clear and meaningful context names to make memory usage easier to understand and debug.
- Always match MemoryContextCreate() with MemoryContextReset() or MemoryContextDelete()
Final Thoughts: Why MCP Matters
MCP might sound like just another tech buzzword, but it’s actually the secret ingredient behind making AI feel more human. With MCP, AI moves beyond reacting — it starts understanding you like a real assistant. It remembers that you prefer bullet points, that you’re allergic to peanuts, or that you’ve already tried option A.
In a world full of information, MCP is what helps AI focus on your world.
So the next time an AI assistant remembers your travel plans or your favorite way to be greeted — thank MCP. It’s quietly changing how we interact with machines, one thoughtful response at a time.
Model Context Protocol (MCP) is still an evolving space, but it’s already shaping how we build smarter, more human-like AI systems. If you’re curious to learn more, here are a few great starting points:
From chatbots that remember your last request to databases that manage temporary memory like a pro — context protocols are the backbone of smarter systems. Whether it’s conversational memory in AI or memory grouping in low-level code, protocols like MCP bring clarity, control, and performance.
As software systems become more complex and memory-critical, structured context management offers a scalable, efficient way to keep memory usage clean and manageable.
