Understanding MCP Roles and Architecture

Imagine you’re assembling a team of robotic assistants to manage a high-tech office. Each robot needs a workspace, a reasoning engine, and a toolkit for interacting with its environment. Initially, every new robot required hardwiring and custom instructions for each task—opening a filing cabinet, sending an email, or logging in to the HR system. This approach quickly became unmanageable: updates, fixes, and scaling grew increasingly difficult as the system expanded.

MCP addresses this challenge by replacing brittle, hardcoded setups with a modular, plug-and-play architecture for AI agents. In this lesson, we’ll explore the architecture of MCP-powered agentic systems—not in abstract terms, but in practical language developers encounter daily: the roles each component plays, how they interoperate, and how this unlocks new possibilities for building scalable, resilient AI applications.

The roles of MCP

In MCP, each software component (an AI agent, plug-in, or backend service) takes on one of three main roles: host, client, or server. These roles specify how components communicate and their responsibilities in the system.

Host

The host is the central orchestrator—the root environment that manages agent lifecycles, user sessions, and connections to tools and data. It might be a desktop app (e.g., Claude Desktop), a web portal, or an IDE such as Visual Studio Code.

Responsibilities

• Session management: Maintains state across user interactions (conversation history, preferences, authentication).

• Security context: Handles authentication, authorization, and user-specific access tokens. Ensures agents only act within allowed boundaries.

Security note: For sensitive deployments, hosts should also enforce sandboxing for servers, use secure channels (HTTPS/TLS), manage credentials centrally, and log all access for auditing.

• Connection orchestration: This involves starting and stopping agent processes, connecting/disconnecting MCP clients and servers as needed, and managing resource cleanup.

• Interface bridge: Translates user inputs into agent-understandable requests, and agent outputs back into UI messages.

• Capability registry: This keeps track of which MCP servers (tools/resources) are currently available and which agents are allowed to use them.

• Hot swapping and discovery: Dynamically loads or unloads new tools/servers at runtime, enabling true plug-and-play extensibility.

Multi-agent orchestration

In many real-world deployments, a single host may orchestrate multiple concurrent agents within the same application or workspace, each with its own client and specialized capabilities. For example, in collaborative AI workspaces (like Claude Team or ChatDev), different agents may handle distinct tasks or represent different personas, all coordinated by the host. MCP natively supports this multi-agent architecture, making it easy to scale from a single assistant to a team of cooperating agents, each managing their workflows and tool access.

Client