Architectural Components of MCP

In the previous section, we discussed the key concepts and terminology of MCP. Now, let’s dive deeper into the architectural components that make up the MCP ecosystem.

Host, Client, and Server

The Model Context Protocol (MCP) is built on a client-server architecture that enables structured communication between AI models and external systems.

MCP Architecture

The MCP architecture consists of three primary components, each with well-defined roles and responsibilities: Host, Client, and Server. We touched on these in the previous section, but let’s dive deeper into each component and their responsibilities.

Host

The Host is the user-facing AI application that end-users interact with directly.

Examples include:

AI Chat apps like OpenAI ChatGPT or Anthropic’s Claude Desktop
AI-enhanced IDEs like Cursor, or integrations to tools like Continue.dev
Custom AI agents and applications built in libraries like LangChain or smolagents

The Host’s responsibilities include:

Managing user interactions and permissions
Initiating connections to MCP Servers via MCP Clients
Orchestrating the overall flow between user requests, LLM processing, and external tools
Rendering results back to users in a coherent format

In most cases, users will select their host application based on their needs and preferences. For example, a developer may choose Cursor for its powerful code editing capabilities, while domain experts may use custom applications built in smolagents.

Client

The Client is a component within the Host application that manages communication with a specific MCP Server. Key characteristics include:

Each Client maintains a 1:1 connection with a single Server
Handles the protocol-level details of MCP communication
Acts as the intermediary between the Host’s logic and the external Server

Server

The Server is an external program or service that exposes capabilities to AI models via the MCP protocol. Servers:

Provide access to specific external tools, data sources, or services
Act as lightweight wrappers around existing functionality
Can run locally (on the same machine as the Host) or remotely (over a network)
Expose their capabilities in a standardized format that Clients can discover and use

Communication Flow

Let’s examine how these components interact in a typical MCP workflow:

In the next section, we’ll dive deeper into the communication protocol that enables these components with practical examples.

User Interaction: The user interacts with the Host application, expressing an intent or query.
Host Processing: The Host processes the user’s input, potentially using an LLM to understand the request and determine which external capabilities might be needed.
Client Connection: The Host directs its Client component to connect to the appropriate Server(s).
Capability Discovery: The Client queries the Server to discover what capabilities (Tools, Resources, Prompts) it offers.
Capability Invocation: Based on the user’s needs or the LLM’s determination, the Host instructs the Client to invoke specific capabilities from the Server.
Server Execution: The Server executes the requested functionality and returns results to the Client.
Result Integration: The Client relays these results back to the Host, which incorporates them into the context for the LLM or presents them directly to the user.

A key advantage of this architecture is its modularity. A single Host can connect to multiple Servers simultaneously via different Clients. New Servers can be added to the ecosystem without requiring changes to existing Hosts. Capabilities can be easily composed across different Servers.

As we discussed in the previous section, this modularity transforms the traditional M×N integration problem (M AI applications connecting to N tools/services) into a more manageable M+N problem, where each Host and Server needs to implement the MCP standard only once.

The architecture might appear simple, but its power lies in the standardization of the communication protocol and the clear separation of responsibilities between components. This design allows for a cohesive ecosystem where AI models can seamlessly connect with an ever-growing array of external tools and data sources.

Conclusion

These interaction patterns are guided by several key principles that shape the design and evolution of MCP. The protocol emphasizes standardization by providing a universal protocol for AI connectivity, while maintaining simplicity by keeping the core protocol straightforward yet enabling advanced features. Safety is prioritized by requiring explicit user approval for sensitive operations, and discoverability enables dynamic discovery of capabilities. The protocol is built with extensibility in mind, supporting evolution through versioning and capability negotiation, and ensures interoperability across different implementations and environments.

In the next section, we’ll explore the communication protocol that enables these components to work together effectively.

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