Understanding MCP: Architectural Learnings from Exploring the Model Context Protocol

The High Level Components

At a high level, the MCP ecosystem has three main components:

1. AI Provider – the brain and decision-maker, like ChatGPT, ClaudeAI, or other LLMs
2. MCP Client – the orchestrator, protocol handler, and dispatcher
3. MCP Server – specialized tool providers that expose capabilities

One key insight: the AI never talks to the MCP Server directly. Every interaction is mediated by the MCP Client.

The Canonical Flow

The canonical flow looks like this:

graph LR
    AI[AI] -->|Decides to use tool| Client[MCP Client]
    Client -->|Routes Request| Server[MCP Server]
    Server -->|Returns Result| Client
    Client -->|Returns Result| AI

Or simply put: AI (decides to use a tool) → MCP Client (routes the request) → MCP Server → MCP Client → AI

Here's what actually happens: The MCP Client exposes available tools to the AI. The AI decides which tools to invoke. The client then routes those requests to the right server and returns the results. Think of it as the AI deciding what to do, and the client handling how to do it.

Local Capabilities

But here's the interesting part: not all capabilities require remote servers. Many actions can be handled through local MCP servers, like:

• Accessing the file system
• Running calculations or parsers
• Querying a local database
• Caching, filtering, or merging data
• Handling lightweight business logic

In this case, the flow becomes:

AI → MCP Client → Local MCP Server → MCP Client → AI

Orchestration

And the real magic happens when you work with multiple servers. A single AI request can leverage several MCP servers, some local, some remote, with the client orchestrating between them:

AI → MCP Client → (Local Server + Remote Server) → MCP Client → AI

This makes the system incredibly fast, flexible, and safe.

The MCP Client is much more than a simple proxy; it's the dispatcher and security boundary, routing tool requests to the right servers and controlling what the AI can access.

Key Takeaways

Key takeaways from my exploration:

• The AI decides which tools to use; the client routes those decisions
• MCP Servers are modular tools, focused and specialized
• Local MCP servers reduce latency and keep data on your machine
• Multi-server orchestration, mixing local and remote capabilities, is where you get flexibility and efficiency
• The client acts as a security boundary; it controls which servers the AI can reach

Whenever you build MCP integrations, ask: "Should this be a local server, or does it need external connectivity?"

I found this perspective extremely valuable for thinking about AI tool integrations, especially when designing systems that combine local logic with external services.

Note: This is a simplified, architectural view of MCP intended to explain mental models and design trade-offs. It intentionally abstracts over protocol-level details such as streaming, multi-step tool execution, and client-specific enforcement behavior.