Key componentsKey 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 layerProtocol 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 layerTransport 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 standard, making them easy to debug and inspect using standard tooling.

Message typesMessage 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:

This initialization process guarantees that both ends of the communication channel understand each other’s capabilities before moving forward.

Message exchangeMessage 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.

TerminationTermination

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 mattersWhy 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 StudioMCP 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, in the Nebius Cookbook repository, where a GitHub analysis agent uses an MCP server and a Model hosted on Nebius (Meta-Llama-3.1-8B-Instruct) to extract insights from given open repositories.

Here’s a quick guide on how to set up and run our example MCP server above:

Clone the repo and install dependenciesClone the repo and install dependencies

git clone https://github.com/Arindam200/awesome-ai-apps.git
cd awesome-ai-apps/mcp_ai_agents/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 or even conda)

Set up environment variablesSet 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 clientRun the client

Execute the script with:

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 companyBring 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 to get early access to resources, personalized guidance, and a dedicated onboarding session.

See MCP in action: Hugging Face Tiny Agents powered by NebiusSee 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: