How to Implement Model Context Protocol (MCP) in Your AI Projects

Building AI applications without a clear context management system is like coding without version control. It works for a while, until it doesn’t. When I used Cursor first time I realized without clear context, agent stucks in a loop that can’t be escaped without clear statefull instructions.

Implementing Model Context Protocol (MCP) properly is the difference between a fragile, chaotic AI experience and a smooth, scalable, intelligent system. It help us to create robust statefull LLM interaction which leads better user experience in many aspects.

Let’s break it down step-by-step.

If you are not familiar with Model Context Protocol check this following post of mine; What is Model Context Protocol (MCP) in LLM Systems?

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Step 1: Define Your Context Types

Start by identifying what kinds of context your application needs to inject into the LLM.

Common examples include:

• User Context: Profile info, preferences, interaction history
• Session Context: Current goal, session state, temporary flags
• Environment Context: Platform, device, location
• Memory Context: Long-term knowledge, retrieved dynamically

Clearly separating these helps avoid mixing up short-term vs long-term vs static context.

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Step 2: Create Standardized Context Models

Each context type should have a well-defined model or schema.

Example (pseudo-code):

interface UserContext {
    userId: string;
    username: string;
    preferredLanguage: string;
    previousInteractions: InteractionSummary[];
}

interface SessionContext {
    currentTask: string;
    sessionStartTime: Date;
    flags: Record<string, boolean>;
}

These structures will later feed the LLM consistently without messy patchwork.

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Step 3: Build a Context Engine

You need a service that:

• Collects context dynamically,
• Merges it when needed,
• Prepares it in a model-friendly way.

This can be a simple class or a more sophisticated microservice depending on your scale.

At each request or interaction, your engine should:

1. Pull latest user/session/environment info,
2. Merge them according to rules,
3. Output a context payload ready for injection.

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Step 4: Integrate Context Into Prompts

The final step is feeding context into the LLM.

You can inject context in different ways:

• System prompts: Attach system-level instructions at the start.
• In-memory augmentation: Merge into ongoing chat history.
• Tool inputs: Pass context when calling function APIs inside LLM workflows.

Example prompt structure:

System: You are a personal finance assistant. The user’s preferred currency is USD.
User: What’s the best investment option for me right now?

The user never said “USD” — the context engine did.

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Step 5: Manage and Update Context Dynamically

Context isn’t static.

As users interact, sessions evolve, or environments change, your context needs to refresh dynamically without restarting everything.

Good MCP implementations support:

• Partial updates (e.g., only updating user mood or task status),
• Timeouts/expiry for sensitive session context,
• Versioning of memory models.

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Final Thoughts

Implementing MCP is not just about better prompts. It’s about designing truly dynamic and responsive AI systems.

Done right, it allows your AI applications to:

• Feel personalized,
• Behave intelligently,
• Scale gracefully as your user base grows.

Context isn’t just data. It’s the soul of AI interaction.

The more your system behaves in a personalized way, the more users will feel connected and the more they’ll keep coming back. Even I, knows this is an illusion, still attracted to how ChatGPT adops my tone and response me the way I feel warm 🙂

I tried to be explicit and descriptive as much as possible I hope you enjoyed the post. See you next time 🙂