Understanding MCP Architecture – The Missing Link Between AI Agents and Tools

In this final post of the Spring Boot MCP blog series, we bring everything together by breaking down the Model Context Protocol (MCP) architecture — a powerful standard that enables AI Agents to communicate seamlessly with tools, APIs, databases, and file systems.

If you haven’t yet, be sure to check out the first two parts:

• Creating a Spring Boot MCP Server
• Building a Spring Boot MCP Client

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What Is MCP?

The Model Context Protocol is an open standard that defines how large language models (LLMs) can delegate tasks to external tools in a structured, reliable way. It enables AI Agents to “invoke” real-world APIs or tools through a well-defined client-server model.

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The MCP Architecture

Here’s an overview of how the MCP system is designed:

The architecture can be broken down into three major components:

1. LLM (Large Language Model)

This includes any language model capable of interpreting natural language and producing output — such as:

• OpenAI
• Gemini
• Ollama (for running local models)

LLMs act as the reasoning layer and are not directly concerned with implementation details like accessing a file or making a YouTube API call.

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2. MCP Host

The MCP Host includes two main components:

• AI Agent: Tools like Claude AI, GitHub Copilot, or your custom UI — which collect prompts from users.
• MCP Client: Responsible for forwarding prompt-based requests to the appropriate tool via an MCP Server.

MCP Hosts typically interact with the LLM to parse user intent and with the MCP Client to execute tasks.

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3. MCP Server

The MCP Server registers tools that represent actual business logic or external APIs. These tools can include:

• Access to the local file system
• Web APIs like YouTube or weather services
• Local or remote databases

The server executes tool invocations and sends results back to the client.

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Communication Flow

The typical data flow in the MCP ecosystem looks like this:

1. User sends a prompt via the AI Agent (e.g., “Search for trending videos on YouTube”).
2. The LLM processes the prompt and identifies the need to invoke a tool.
3. The MCP Client receives this context and forwards it to an MCP Server.
4. The MCP Server executes the corresponding tool (e.g., YouTube API integration).
5. The result is returned back through the same path to the user.

MCP Clients and Servers communicate using gRPC, ensuring low-latency, high-efficiency message passing.
When running on the same machine (during development or testing), the communication may default to stdio, making local experimentation extremely lightweight.

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Real-World Example

Imagine you’re using Claude AI (running in your MCP Host). You ask:

“Fetch me the latest videos on AI from YouTube.”

Here’s what happens:

• Claude forwards the prompt to the LLM.
• The LLM uses the MCP Client to reach out to a YouTube Tool registered with the MCP Server.
• The tool hits the YouTube API and returns a response.
• The AI Agent formats and displays the result to the user.

This entire loop happens within milliseconds — abstracting all the underlying complexity from the end-user.

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Why This Matters

The MCP architecture unlocks powerful possibilities:

• Modular Integration: Add or remove tools (file system, API access, etc.) without changing your AI logic.
• LLM Agnostic: Works across OpenAI, Gemini, Ollama, and more.
• Developer Friendly: Lightweight protocol (gRPC/stdio) with Spring Boot support for both client and server.
• Production-Ready: Secure, scalable, and cloud-deployable architecture.

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Final Thoughts

With this final post, we wrap up the MCP blog series — where we built:

• An MCP Server using Spring Boot to register tools
• An MCP Client that talks to AI Agents and connects to MCP Servers
• A complete understanding of the MCP ecosystem

Whether you’re building intelligent assistants, internal dev tools, or automating workflows, MCP offers a rock-solid foundation to plug AI into the real world.