Building a Modern Streaming Service: MCP Architecture and Technology Choices

Background

AI agents have the potential to fundamentally transform how individuals manage their subscriptions across various services such as streaming platforms, software applications, news outlets, and more.

This post will share the experience of implementing a prototype streaming service with support for MCP (Model Context Protocol), to demonstrate that AI Agents and MCP is a possible future for streaming services.

The prototype was implemented containing services that provides interfaces for managing content, users, and subscriptions. All services exposing tools in a standardized way for LLMs to connect to the different services

What is MCP

MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.

MCP helps you build agents and complex workflows on top of LLMs. LLMs frequently need to integrate with data and tools, and MCP provides:

• A growing list of pre-built integrations that your LLM can directly plug into
• The flexibility to switch between LLM providers and vendors
• Best practices for securing your data within your infrastructure

MCP follows a client-host-server architecture, where hosts (LLM applications) initiate connections, clients maintain 1:1 connection with servers, and servers provide context, tools, and prompts to clients. This architecture ensures clear security boundaries and isolation of concerns, allowing for flexible and extensible communication between components.

The implementation was specifically designed to prove that MCP can be effectively used for building streaming services, by showcasing how its structured, model-based approach can handle user management, content delivery, and subscriptions.

Leveraging Cursor and Claude for Development

A significant portion of the service development was powered by tools like Cursor and Claude. Cursor provided an efficient development environment that enabled seamless integration of AI assistance into the workflow.

Claude, an advanced language model, played a pivotal role in generating and refining code. Claude made it possible to rapidly prototype ideas, explore multiple implementation approaches, and even draft entire functions with contextually accurate logic.

Its ability to handle nuanced technical prompts ensured that the generated code was not only syntactically correct but also adhered to best practices for scalability and maintainability. This combination of tools reduced development time significantly and allowed me to focus on higher-level architectural decisions.

Core Architecture

A TypeScript-based stack with modern tooling and excellent IDE support to streamline development. It also allowed easy integration with MCP as a Typescript SDK is provided by MCP.

The service is built around several core components:

User Management

• Handles user authentication and profiles.
• Manages user preferences and watch history.
• Integrates with the subscription system.

Content Management

• Supports multiple content types (movies, series, sports).
• Handles metadata and asset management.
• Controls content visibility and access rights.

Subscription System

• Manages different subscription tiers.
• Handles billing and payment processing.
• Controls access to premium content.

Asset Management

• Manages video assets and poster images.
• Supports multiple quality levels.
• Optimizes content delivery through adaptive streaming.

Databases

For user and content data, MongoDB was selected due to its flexibility and scalability. For poster images and video assets, an object store database was used by the MinIO storage service through Eyevinn Open Source Cloud. This setup ensured efficient storage and retrieval of large files, particularly for high-resolution video assets and associated metadata.

For more information see: https://blog.osaas.io/2025/01/09/simplify-your-file-storage-with-open-source-cloud-and-minio/

MCP Implementation

MCP’s core concept is its tool-based interface. Each tool represents a specific capability of the service and is defined with:

• A clear description of its functionality.
• A structured input schema specifying required and optional parameters.
• An explicit output schema detailing expected results.
• Robust error-handling mechanisms to ensure reliability.

By standardizing these interactions, MCP promotes consistency across the service’s components and makes it easier to add, modify, or scale capabilities.

Key Tools Implemented

To validate MCP’s effectiveness for streaming services, the prototype implemented the following tools:

• User Management Tools: Handle user creation, updates, deletion, and authentication.
• Subscription Tools: Manage subscription tiers, billing, status checks, and cancellations. Provide detailed pricing information based on subscription tiers.
• Content Discovery Tools: Enable users to search, browse, and discover content.

Each tool adheres to MCP’s model-driven philosophy, making them self-contained, reusable, and easy to integrate.

To play around with MCP Supergateway runs a MCP stdio-based servers over SSE (Server-Sent Events) with one command. This is useful for remote access, debugging, or connecting to SSE-based clients when your MCP server only speaks stdio. Available through Open Source Cloud: https://app.osaas.io/browse/supercorp-ai-supergateway

Conclusion

Model Context Protocol (MCP) stands out as a recommended framework for enabling large language models (LLMs) and AI agents to interact with streaming services. By defining structured, context-aware tools with standardized inputs and outputs, MCP ensures seamless and reliable integration of AI-powered capabilities into complex systems like streaming platforms. The MCP architecture not only supports scalability and security but also makes adding or modifying features straightforward.

The use of AI tools like Cursor and Claude to develop the prototype service played a critical role in accelerating the prototyping and implementation phases. These tools made it possible to focus on domain-specific challenges and higher-level architectural decisions rather than getting bogged down by boilerplate code or repetitive tasks.

For teams exploring ways to integrate AI-driven solutions into their platforms, MCP provides a robust foundation, and leveraging AI development tools can significantly enhance both productivity and innovation.