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Creating Custom AI Agents
Creating Custom AI Agents
MCP Architecture
To build a flexible and intelligent system with MCP, you need to understand how its key components work together. Each part of the architecture has a specific role—from receiving user input to executing logic and returning results. Here's a breakdown of the core elements that make the MCP framework powerful and adaptable.
- The heart of the system. It listens for incoming requests, processes context, and routes them to the correct function or service. Think of it as your custom backend.
- These are interfaces or tools (like Excel, web apps, or command-line tools) that send requests to your server—often triggered by a user or AI assistant.
- A structured snapshot of the user’s environment, intent, or task. MCP uses this to understand what needs to be done and how.
- These are your predefined handlers—for example,
draft_email()that perform logic based on incoming context.
Let’s walk through a typical scenario to understand how the components of the MCP architecture work together with AI. Imagine a user typing:
The client (a voice assistant, desktop tool, or web app) sends a command to the MCP server, which immediately enriches the request with context by pulling in the user's email credentials, locating the correct inbox, retrieving the five latest messages, and tagging any priorities or deadlines.
With context ready, the AI model (like Claude or GPT) is called in. It reads each email, identifies tone and purpose such as a meeting request, follow-up, or complaint, and drafts suitable replies like a meeting confirmation, thank-you note, or task update.
The server compiles the drafts, optionally lets the user preview them, and returns them to the client ready to send, edit, or schedule.
To the user, the process feels seamless. Behind the scenes, it's a carefully coordinated system.
A client issuing a high-level task;
The MCP server gathering context and invoking a function;
AI interpreting the request and producing tailored results.
That's the power of combining context, logic, and language intelligence in a unified architecture.
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