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How to Use Model Context Protocol in Your AI Product?

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If you’re building something with LLMs (maybe a support copilot, an AI layer inside your SaaS product, or an agent that pulls data from tools), you’ve probably hit that moment: “Why does this model keep forgetting everything?”

Well, Model Context Protocol (MCP) is the answer.

It’s a structured way to connect your product’s real-world state with your AI model’s brain. MCP acts like a bridge that links memory, user context, tool outputs, goals, and session history to whatever model you’re using.

This blog walks you through exactly how to use MCP in your own product with just real, usable steps.

TL;DR:

Model Context Protocol (MCP) is a structured way to give AI models the real-world context they need, like memory, tools, user goals, and session history, so they behave more intelligently and consistently. This guide shows how to integrate MCP into your product by building a middleware layer that composes and delivers this context to your LLM. Key steps include mapping LLM entry points, structuring payloads, integrating tools and memory, and persisting thread state. MCP boosts conversation flow, tool coordination, and goal-awareness while making debugging easier. While you can build it in-house, expert support (like Azilen’s) ensures a scalable, production-ready implementation.

Where MCP Fits in Your Architecture

Let’s break it down visually:

MCP Architecture

You’ve already got a UI or backend triggering model calls. The model gives you something back – sometimes smart, sometimes confusing.

MCP steps in between and structures the conversation:

✔️ It gathers memory

✔️ Pulls tool results

✔️ Packs metadata about the user’s intent

✔️ Describes the current state of the thread

✔️ And ships it all cleanly into the model’s input space

This structure gives your LLM proper context, without hacking prompts or juggling chains manually.

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What to Integrate Through MCP

Your model can be smart, but only if it’s given the right context in the right format. Here’s what you should plug into MCP:

HTML Table Generator
Component
Purpose
Memory Store (vector DB, summaries) Retrieves relevant history or knowledge base
Tool Outputs (API calls, calculators, RAG responses) Feeds external data directly into the model
User Metadata (goals, plan, preferences) Keeps conversations personalized and aligned
Thread State (previous messages, actions) Gives multi-turn understanding and memory
Plans (task steps or goals) Helps the model reason toward an objective

How to Structure Context Using MCP

MCP works through a structured payload format. Think of it like sending a well-packed context box to your model every time it’s called.

Key objects in that box:

➜ Thread: Session or conversation container (usually scoped to a user + task)

➜ Messages: Individual inputs/outputs with metadata

➜ State: App-level info the model should know (preferences, retrieved data)

➜ Tools: What the model can use (APIs, functions, plugins)

➜ Plan: What this interaction is trying to accomplish

Steps to Implement MCP in Your Software Product

Let’s break this into 5 clear steps. If your team has already built LLM features, this will feel familiar, but way cleaner.

Step 1: Start by Mapping Your LLM Entry Points

Chatbot? Form assistant? Search? Internal ops tools?

For each entry point, identify:

What data the model needs to understand the task?

What memory or history should be carried forward?

Which APIs or tools are being used (or could be)?

You’re trying to spot all the context touchpoints where MCP can step in.

Step 2: Build the MCP Integration Layer

This sits between your app and the model. It pulls memory, tool data, metadata, and turns them into an MCP payload.

You can build this layer using:

A middleware service (Node.js, Python, Go, whatever your backend runs on)

A custom orchestrator using LangChain, LangGraph, or CrewAI-style agent runners

Or by extending your LLM router layer if one already exists

Keep this layer stateless. It should compose context, not hold it.

Step 3: Structure the Payload Using MCP Format

Now comes the core value of MCP. Here’s what your context payload might include:

HTML Table Generator
Object
Description
thread Unique ID + metadata for the session (user, task, goal)
messages The interaction history—role-tagged inputs/outputs
state Current memory, user profile, task progress, RAG content
tools APIs or functions available to the model (with schema)
plan The high-level instruction/goal guiding this interaction

Step 4: Wire the MCP into Your LLM Calls

Once the payload is ready, pass it to your model.

If you’re using OpenAI/Anthropic APIs:

Use the “messages” + “tool_calls” formats

Inject state and plan directly into the system message or context fields

Keep memory token-efficient, summarize when needed

If you’re running open-source models:

Embed the MCP context into prompt preambles

Use formatting layers to translate JSON into readable instructions

Step 5: Process Model Output & Trigger Actions

The model might return: a natural language answer, a function call, or a reasoning step.

Your job here is orchestration. Route the output to:

Your frontend (chat, UI)

A backend action (API, DB update)

A second LLM call if more reasoning is needed

Then, update memory and thread state. This keeps future calls aligned with the past.

Step 6: Persist Thread State and Memory

MCP doesn’t decide where memory lives; it just makes sure it’s delivered to the model cleanly.

You can store:

Interaction history in Postgres or Firestore

Summarized memory in a vector store

Tool output metadata in Redis or Mongo

That way, every time a user returns or the agent continues, the context is fresh and relevant.

Step 7: Test with Live Threads and Edge Cases

Time to stress test.

Try full workflows:

Start a thread

Call multiple tools

Let the user shift topics mid-way

Resume a task after time passes

This is where MCP proves its value: memory flows, tools align, and models stay grounded.

Log everything. Tune your prompt formatting. Optimize the size of your payloads to stay within token limits.

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Quick Wins You’ll Notice Instantly

Once MCP is wired up, things get better fast:

• Conversations flow naturally

• Models remember goals and past questions

• Tools work inside AI responses

• Agents become task-aware, not just prompt-aware

• Debugging context becomes easier (no more “why did it say that?”)

You’ve moved from a patchwork of hacks to a clean integration protocol.

Things to Watch Out For

MCP solves a lot, but it still lives inside a fast-moving ecosystem. There are a few key areas where it pays to stay intentional:

Context Governance

You’re managing memory now. So, make sure you know what’s being stored, for how long, and by whom.

Design your thread state with expiration rules, PII masking, and access controls baked in.

Payload Size

LLMs still care about token limits. If you load 15 past messages, 3 tool responses, and a long plan – all in one call – you’re hitting limits fast.

Use summarization, context compression, or selective recall to keep your payloads lean.

Versioning and Compatibility

Your tool schemas, memory formats, and API wrappers might evolve.

Structure your MCP layer to version those gracefully so old threads still run even when your tools grow smarter.

Debuggability

Once you wrap logic in MCP, it helps to log and trace every payload.

Build in logging for raw model inputs (structured payload), final outputs, memory lookups, and tool activations. This makes fine-tuning, auditing, and debugging easier later.

Build vs. Buy: Should You Build Your Own MCP Layer?

At first glance, building your own MCP layer sounds doable.

You’ve already got APIs, tools, maybe even vector stores. Stitching things together seems like a weekend project. But here’s what usually happens:

The LLM works.

Then the context gets messy.

Then the memory breaks.

Then the tools fail to coordinate.

And now you’re debugging invisible payloads at 2 AM.

Building it Yourself

Yes, you can build a custom layer. And for internal prototypes, that might work.

But as your product grows, so does the complexity:

✔️ Context starts fragmenting across services

✔️ You end up managing 3+ orchestration pipelines

✔️ Versioning payloads, tools, and memory gets hard to scale

✔️ Every new AI feature adds integration debt

Teams lose momentum here. Fast.

Buying MCP Expertise: Clarity, Speed, and Stability

Working with a team that lives and breathes AI system design can shift the equation completely.

Here’s what you unlock when you bring in MCP integration experts like Azilen:

✔️ Clean architecture from day one

✔️ Production-grade context design

✔️ Integration across memory, tools, and user logic—done right

✔️ Reduced engineering load, no reinventing orchestration logic

✔️ Faster path from LLM idea → context-aware AI product

You stay focused on the product. We handle the protocol.

Why Azilen?

Azilen is an enterprise AI development company. We help AI-native and AI-augmented products go beyond prompts.

We design and build:

➡️ MCP-aligned orchestration layers

➡️ Context-first AI features that scale

➡️ Agentic systems that coordinate tools, threads, and plans

➡️ Memory strategies that don’t collapse at scale

And we do it with production thinking – latency-aware, cost-aligned, and compliance-ready.

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Top FAQs on Model Context Protocol

1. What is Model Context Protocol (MCP) and why is it important?

MCP is a structured way to pass all relevant context, like memory, user state, tools, and session history, into an AI model. It ensures your LLM doesn’t “forget” things between messages and can reason more intelligently over tasks and tools.

2. How is MCP different from traditional prompt engineering?

Traditional prompt engineering manually packs context into a prompt. MCP formalizes this process using structured payloads, making it more scalable, maintainable, and easier to debug as your product grows.

3. What kind of data should be passed through MCP?

You should include:

➜ Memory (conversation history, vector store retrievals)

➜ User metadata (goals, preferences)

➜ Tool outputs (API responses, RAG data)

➜ Thread state (multi-turn dialog flow)

➜ Plans (tasks or objectives the model is pursuing)

4. How does MCP improve LLM performance in production?

MCP keeps your LLM grounded in real context. You’ll notice:

➜ More coherent conversations

➜ Goal-aware agents

➜ Better tool utilization

➜ Fewer hallucinations

➜ Easier debugging and auditing

5. What are the risks or limitations of MCP?

Key challenges include:

➜ Token limits (payloads can get large)

➜ Governance (you manage sensitive user data)

➜ Versioning (tools and schemas evolve over time)

➜ Debugging (requires structured logging)

Glossary

1️⃣ Thread: A logical container representing a user session or ongoing task. It holds messages, goals, memory, and tool usage over time.

2️⃣ Messages: Individual conversational turns (inputs and outputs) tagged with roles like user, assistant, or tool. Helps maintain coherent dialog and task history.

3️⃣ State: The current context snapshot of the user, task, or environment, such as preferences, current goal, retrieved knowledge, or selected options.

5️⃣ Tools: APIs, functions, or plugins that your model can use during its reasoning or execution (e.g., pricing APIs, database queries, calculators).

5️⃣ Plan: A high-level intent or goal for the interaction (e.g., “compare suppliers”, “summarize meeting notes”) that helps the model stay aligned across steps.

Swapnil Sharma
Swapnil Sharma
VP - Strategic Consulting

Swapnil Sharma is a strategic technology consultant with expertise in digital transformation, presales, and business strategy. As Vice President - Strategic Consulting at Azilen Technologies, he has led 750+ proposals and RFPs for Fortune 500 and SME companies, driving technology-led business growth. With deep cross-industry and global experience, he specializes in solution visioning, customer success, and consultative digital strategy.

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