Understanding the Model Context Protocol: Architecture
Understanding the Model Context Protocol: Architecture
As LLM-powered agents become more complex, integrating them with tools, APIs, and private data sources remains a major challenge. Model Context Protocol (MCP) offers a clean, open standard for connecting language models to real-world systems through a modular, plug-and-play interface. In this article, we explore how MCP works.
Introduction
Model Context Protocol (MCP) is an open protocol that defines a standardized way for applications to provide context to large language models. Think of it as the USB-C for AI apps, instead of reinventing the wheel every time you want your LLM to access files, APIs, or tools, MCP gives you a common plug-and-play system.
In practical terms, MCP allows your LLMs to communicate with different data sources (like databases or local files) and tools (like APIs or scripts) using a unified protocol. This is key when building agents, copilots, or any AI-driven workflow or system where the model needs to “see” external context or take some action. So rather than hard-coding integrations, MCP lets you set things up in a modular way, so if you switch models or tools later, your architecture stays unchanged.
Architecture
The architecture behind MCP is designed to be modular, scalable, and adaptable across different LLM applications and environments. At its core, MCP follows a client-server model that helps large language models securely access external context and tools, without hard-wired integrations.
At a higher level, MCP revolves around three main components:
- Hosts: Applications like Claude Desktop or IDEs that initiate communication. These are where the LLMs live.
- Clients: Lightweight protocol clients embedded within hosts. Each client maintains a 1:1 connection with a server.
- Servers: Independent processes that expose capabilities such as data access, tools, or prompts, over the MCP standard.
This setup enables LLMs to dynamically interact with external systems via structured protocols rather than brittle, application-specific logic.
Key components
The MCP is built around several well-defined components that enable clean, extensible, and bidirectional communication between AI applications and external systems. Each layer is designed to be modular and protocol-agnostic, allowing developers to tailor implementations without sacrificing interoperability.
Protocol layer
At the heart of MCP is the protocol layer, which handles the framing of messages, mapping of requests to responses, and delivery of notifications.
Whether you’re working in TypeScript or Python, the protocol interface provides methods for:
- Setting request/notification handlers
- Sending structured requests
- Receiving responses or asynchronous notifications
In TS:
class Protocol<Request, Notification, Result> {
setRequestHandler<T>(schema: T, handler: (req: T) => Promise<Result>): void
setNotificationHandler<T>(schema: T, handler: (note: T) => Promise<void>): void
request<T>(req: Request, schema: T): Promise<T>
notification(note: Notification): Promise<void>
}
In Python:
class Session(BaseSession[RequestT, NotificationT, ResultT]):
async def send_request(self, request: RequestT, result_type: type[Result]) -> Result: ...
async def send_notification(self, notification: NotificationT) -> None: ...
These interfaces allow LLM-driven clients to exchange structured data with context providers using consistent logic and type safety.
Transport layer
The transport layer defines how messages move between the client and the server. MCP supports multiple transport protocols, including:
- Stdio: Best suited for local processes; communicates over standard input/output (e.g, you want to retrieve a list of files in a directory, this could be used)
- HTTP + SSE (server-sent events): Ideal for networked services or remote integrations.
Regardless of the transport used, MCP messages follow the JSON-RPC 2.0
Message types
MCP uses four message types to structure communication:
Request:
interface Request {
method: string;
params?: { ... };
}
Result:
interface Result {
[key: string]: unknown;
}
Error:
interface Error {
code: number;
message: string;
data?: unknown;
}
Notification:
interface Notification {
method: string;
params?: { ... };
}
These types ensure clarity in all exchanges. Developers can easily trace the origin and outcome of every interaction across systems.
How it works
At runtime, MCP enables real-time interaction between an LLM application and an external system through a clearly defined connection lifecycle. This lifecycle ensures that communication between clients and servers remains robust, predictable, and easy to manage.
Let’s break down the full sequence from connection setup to termination.
Initialization phase
Before any communication begins, the client initiates a handshake with the server to negotiate protocol compatibility and capabilities.
Here’s a step-by-step rundown:
- The client sends an initialize request to the server. This includes the client’s protocol version and supported features.
- The server responds with its own version and advertised capabilities.
- Finally, the client sends an initialized notification to acknowledge the handshake is complete.
At this point, the connection is considered fully established, and both sides are ready to begin exchanging messages.
This initialization process guarantees that both ends of the communication channel understand each other’s capabilities before moving forward.
Message exchange
Once initialization is complete, the client and server enter the active communication phase.
There are two supported message types during this phase:
- Request-response: Used when one side expects a structured reply.
- Notification: One-way messages that don’t require acknowledgment.
Either the client or the server can initiate requests. This bi-directional pattern allows for more flexible integrations, like streaming context from a server to the LLM or pushing status updates from the client to the server.
Termination
Connections can be shut down normally or interrupted by an error. There are three primary termination paths:
- Graceful close: One party calls a close () method or equivalent to end the session.
- Transport interruption: Disconnection due to underlying transport failure (e.g., pipe closes, network issues).
- Error conditions: When unrecoverable errors occur (e.g., protocol violations), either side can initiate a shutdown.
Implementations should always account for cleanup, resource deallocation, and state persistence if needed during termination to avoid leakage or inconsistent behavior.
Why it matters
MCP isn’t just another developer abstraction, it solves real, persistent challenges in AI system design. Today’s LLMs are often deployed in environments that demand integration with tools, APIs, and private data sources. Without a standard like MCP, every integration becomes a custom job, increasing complexity, risk, and maintenance overhead. MCP changes that. It introduces a vendor-neutral interface that allows teams to swap models, upgrade tooling, and manage context flow without refactoring entire stacks.
For companies, this translates to faster prototyping, cleaner infrastructure, and better governance. Security also benefits: because MCP interactions are host-controlled, data never flows unchecked from model to resource. Organizations can enforce access boundaries, validate inputs, and monitor all activity, critical for staying compliant with privacy regulations and internal policies. If you’re building systems where LLMs need access to dynamic context or need to trigger actions across services, MCP is not just an option, it’s a foundation.
MCP clients with Nebius AI Studio
Nebius AI Studio provides access to powerful open-source LLMs that can be used to drive MCP-based applications. While Nebius doesn’t implement MCP itself, its models can serve as the inference engine behind your MCP clients, handling structured requests, executing tool calls, and generating responses in real-time.
If you’re building a client that needs to communicate via MCP and use a hosted LLM for reasoning, Nebius is a solid choice for inference. A working example of this is available here
Here’s a quick guide on how to set up and run our example MCP server
Clone the repo and install dependencies
git clone https://github.com/Arindam200/Nebius-Cookbook.git
cd Nebius-Cookbook/Examples/MCP-starter
pip install -r requirements.txt
This will install the core dependencies: openai-agents, python-dotenv, openai.
NB: Please make sure you have Python 3.10+ installed (we recommend using a Python version manager like pyenv
Set up environment variables
Create a .env
file in the same directory with the following values:
NEBIUS_API_KEY=your_nebius_api_key
GITHUB_PERSONAL_ACCESS_TOKEN=your_github_token
This enables the script to authenticate both with Nebius’ API and GitHub’s API to analyze issues and commits.
Run the client
Execute the script with:
python main.py
You’ll be prompted to enter a GitHub repo (it should be in the format owner/repo, e.g., Arindam200/logo-ai).
The system will spin up an MCP client, connect to the GitHub MCP server using this
The results are then printed in the terminal, showing what the model found and how it interpreted the repository data. Below is a video showcasing what you get after running the demo code:
This example is a solid foundation for anyone looking to build context-aware agents using Nebius and MCP. You can easily adapt it to other tools or workflows by switching out the server or modifying the agent instructions.
Bring MCP to your company
If you’re interested in implementing MCP-powered workflows inside your company — whether to build smarter agents, copilots, or integrate dynamic context into your AI systems — we can help.
We’re gathering early adopters who want to explore how Nebius AI Studio models can power MCP-driven applications. Register your interest
See MCP in action: Hugging Face Tiny Agents powered by Nebius
Hugging Face recently showcased Tiny Agents, a lightweight MCP-powered agent built in just 50 lines of code — using Qwen2.5-72B-Instruct hosted on Nebius as the default model.
You can try it yourself with one command:
npx @huggingface/mcp-client
This project shows how MCP dramatically simplifies building agentic applications by standardizing tool access — and why optimization for tool use and function calling is the next major wave in AI applications. Check out the full walkthrough in the Tiny Agents blog post
Connect with our team if you’re looking to implement MCP and dynamic tool use in your company. We’d love to help.
It’s clear that MCP offers a clean, modular way to give LLMs access to real-world tools and data, securely and at scale. For teams building agentic systems, complex pipelines, or custom copilots, adopting MCP is a forward-compatible move. And with platforms like Nebius AI Studio, getting started is easier than ever.