How to Use Model Context Protocol in Your AI Product?

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.

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.

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

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

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.

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.

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.

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.

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.

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.

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.

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)

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

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)

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.