The Technical Backbone of MCP: Architecture & How It Works

Introduction

MCP stands for Model Context Protocol, which standardizes how an application provides context to LLMs. It makes the integration of AI assistants with external data sources simpler with its plug-and-play interface.

One of the major challenges while integrating with external data sources was multiple custom integrations, which complicated the task. But, with MCP, this challenge has been resolved as it provides a single standard to integrate multiple data sources.

Let’s dive deeper into the architecture and working of MCP.

What is the Architecture of MCP?

The MCP architecture consists of 3 components: Host, Client, and Server. Client is inside the Host and is called “Host with Client”. Let’s understand the functions of each one of them:

1. Host with Client

Host is where the interaction originates—this could be a web app, chat interface, integrated development environment (IDE), or even a voice assistant. The host includes the MCP Client, responsible for:

• Capturing the user intent
• Refining it into a well-defined query format
• Sending this query to the server via the Protocol that acts as a transfer layer.

We can say that the MCP Client is an interface layer that is responsible for managing one-on-one connections with MCP servers and even handles security or authentication.

2. MCP Server

MCP Server is an integration and execution layer that is connected with multiple data sources, both local (files, databases, internal APIs) and Remote (anything on the internet) to provide information to the host. It is responsible for:

• Receiving requests from the client
• Interfacing with external tools (e.g., APIs, databases, knowledge systems)
• Structuring retrieved data into model-readable context
• Returning structured responses for LLM processing

Thus, it provides a unified interface to the model, ensuring consistency and scalability.

How does MCP Work?

Let’s break down the lifecycle of a typical MCP interaction:

1. User Query Initiation
   A user enters a query through a front-end interface that can be an AI model, a chat interface, a virtual assistant, a web app, or anything. (e.g., “What’s the revenue for Q1?”).
2. Client Encodes the Request
   The host’s MCP client structures the query as a request object following the MCP format and sends it to the server.
3. Server Retrieves External Context
   The server receives the query and determines which external tools or systems to consult—this could be an internal financial API, a SQL database, or a content management system. It pulls only the relevant data needed to answer the query.
4. Context Sent to the LLM
   The retrieved data is formatted into a context block and sent alongside the original query to the LLM. The LLM now has both natural language and structured context to reason from.
5. LLM Generates a Response
   With access to real-time context, the model generates an accurate, informed answer—grounded in actual data, not guesses.
6. Response Returned to Host
   The final response is returned to the client and displayed to the user in the appropriate interface.

This process is repeatable, auditable, and scalable. Importantly, data never needs to be hardcoded into the model, allowing for continuous updates without retraining.

Conclusion

MCP is a major leap forward in how AI models interact with information. The introduction of MCP has fixed the integration mess, streamlining the information retrieval process. This means that businesses need to invest in solutions that provide MCP integration.

To help you with this, SearchUnify provides AI Agents that can leverage Model Context Protocol (MCP) to enhance automation, streamline integrations, and optimize customer support workflows.

Ready to take a step further into your customer support automation?

Book a call with our experts today!