Demystifying MCP: A Visual Guide to Multi-Component AI Systems
In this post, we'll break down what MCP really is, how it connects the moving parts of an AI system, and where the LLM (the model) actually runs. And yes — we'll do this visually.
The Model Context Protocol (MCP) is revolutionizing how AI connects with the digital world by offering an open, standardized way for models to interact with external systems—much like the USB-C of AI. Instead of building endless one-off integrations, developers can now "plug in" their AI models through a single, universal protocol that securely links them to databases, tools, and workflows. At its core, MCP defines a standard interface that connects AI applications to external systems called servers, while the AI models themselves act as clients that request context or perform actions. This means that once an MCP-compatible server exists for a particular tool or dataset, any AI application that supports MCP can immediately use it without additional integration work. For developers, this simplifies architecture and accelerates development by ensuring compatibility across different systems, reducing complexity, and unlocking the ability to build context-aware applications with minimal setup. For end users, it delivers smarter, more capable assistants—AI systems that can understand intent, access live data, and perform real actions like scheduling meetings, generating apps, or analyzing business insights within a single conversational flow. Just as USB-C transformed how devices share power and data through one universal connector, MCP brings standardization, interoperability, and universality to AI connectivity—bridging the gap between intelligence and execution, and turning AI from a clever conversationalist into a true, context-aware partner in everyday workflows.
Global Building Blocks
Every MCP-based system starts with a few global building blocks that everything else depends on.
1. LLM Host / Model (The Brain)
This is the intelligence behind the system — your Large Language Model (LLM).
In most real-world setups, the model lives on the server side. The MCP Client doesn't run the model; it talks to it.
The model's job is to understand prompts, decide what to do next, and coordinate tools or data sources to deliver a meaningful response.
In short: the Client asks, the Server (and the model) think, and then respond.
2. Context (File, Storage, Memory)
To make smart decisions, the AI needs memory — access to the right files, settings, or past information.
The Context component gives it that memory layer. Whether it's a file system, shared storage, or short-term session memory, this is where the LLM pulls the facts it needs to reason effectively.
3. Protocol (The Rulebook)
Protocols define how everything talks. MCP uses open standards (like JSON-RPC) to ensure consistent communication between the model, client, and server.
Think of it as the grammar that keeps conversations between AI components structured and reliable.
Client ↔ Server: How It All Comes Together
Once you understand the global pieces, the next step is how they interact. The MCP architecture follows a Client–Server pattern.
The MCP Client
This is the part you actually use — your IDE, terminal, or app interface.
It's responsible for capturing prompts, preparing requests, and coordinating small local actions.
Core parts of the Client:
- Prompt: The starting point — what you tell the system to do.
- Client Logic / Interface: The layer that transforms natural input into structured MCP requests.
- Local Tools: Fast actions that can happen on your device — like reading a local file or displaying output.
Once the client understands what you need, it sends that structured request to the MCP Server.
The MCP Server
This is where the heavy lifting happens.
The server interprets client requests, invokes the LLM (often running on the cloud or a hosted environment), and manages access to shared tools and resources.
Key parts of the Server:
- Protocol: Keeps everything in sync with the MCP standard.
- Tools: Advanced operations or API integrations that the model can call — for example, a deployment API or database query.
- Resources: The underlying data or services those tools interact with.
Together, the client and server form a conversation loop — the client initiates, the server processes, and the model drives the reasoning.
Real-World Implementations
What makes MCP so exciting is how adaptable it is. You'll see it in everything from code assistants to enterprise automation systems.
1. Claude Code
A terminal-style app that acts as an MCP Client. It talks to the Claude LLM running on Anthropic's servers, and uses dedicated MCP Servers (like Git, Filesystem, or Deployment) to execute real actions such as reading files or deploying code.
2. VS Code Integrations
Extensions like Copilot work as local MCP Servers.
They expose your workspace so an AI client can use tools like read_file or write_file — effectively letting the model "edit" your project directly through MCP.
3. Custom .NET MCP Implementations
In enterprise environments, developers build custom MCP apps that bridge old APIs or internal systems. A .NET app might act as an MCP Server exposing tools (like legacy C# logic or databases), while an AI Client connects to it through the same standardized protocol.
The Bigger Picture
The beauty of MCP lies in its standardization. It doesn't matter where your LLM runs — on-prem, cloud, or local — the protocol ensures every component can talk to each other securely and predictably.
By combining model reasoning with structured tools and memory, MCP turns an LLM into something more powerful than just a chatbot — it becomes an intelligent system that can read, act, and learn across multiple contexts.
In short, MCP isn't just another technical acronym.
It's the glue that holds together the future of agentic AI — one where models don't just respond but truly collaborate with tools and data around them.
If you're experimenting with AI systems or building your own assistants, understanding MCP is a great step toward creating smarter, more modular architectures that scale.
In the next blog, I'll take a more hands-on approach and explore the MCP .NET Library, walking through how you can build your own MCP Client and Server using .NET to connect your LLM to real-world tools and business logic.
