How Organizations Use Model Context Protocol
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Businesses of all kinds are focusing on developing utility-boosting applications based on large language model (LLM) use and generative AI (GenAI) principles. Venturing into this new technology area can yield rewards as those apps produce versatile new experiences for users — but working with such complex, open-ended technology comes with challenges.
The Model Context Protocol (MCP) is designed to simplify development by providing a standardized toolkit for connecting LLMs and AI programs to external data sources and workflows. Equipped with this capability, MCP users can apply multimodal AI tools to a wide range of use cases, including working with large unstructured datasets such as video files.
What Is Model Context Protocol and How Does It Work?
MCP is an open-source protocol whose developers envision it as “a USB-C port for AI applications.” What this means in practice is that by acting as a consistent standard, MCP allows users to connect large language models with external data source options, tools, and specialized agentic workflow offerings to help them create novel AI applications.
By helping the various components of new, bespoke GenAI apps connect with one another, MCP integration makes it easier to create more ambitious digital products with less time-consuming and difficult coding.
The creators of the protocol emphasize that this easier, more streamlined process helps every party. Developers gain the ability to produce more capable, specialized AI app releases on shorter timelines, while the end-users of those applications receive a better product that will help them in their day-to-day tasks.
MCP has already existed long enough that developers have come up with best practices for using it, ensuring that it can achieve its purpose to connect AI apps, data sources, and workflows without becoming too resource-intensive.
How MCP Works
The actual technical use of MCP involves using intermediary dedicated servers, either remote or local MCP servers, to provide contextual information for an LLM. This added information helps the AI model process the data it’s ingesting. Such extra recognition can be a powerful capability, saving the language model from running unnecessarily complex calculations.
MCP has two layers, the data layer and the transport layer. The data layer, based on a JSON schema, clarifies the nature of an LLM’s fuel, while the transport layer is for communication between an AI application and the MCP servers. It also uses three “primitives,” which are the kinds of information it can exchange. These are:
- Tools: Executable functions from an AI application.
- Resources: External data source options providing context.
- Prompts: Templates that shape LLM interactions.
Key Model Context Protocol Use Cases
MCP adoption is a powerful concept on paper, but how can developers and end-users benefit from it in practice? The value comes through in the long and growing list of use cases for the protocol within the MCP ecosystem. A given situation may call for an MCP deployment use case envisioned by its creators and included in their knowledge base, while others could rely on a novel option discovered by ambitious developers.
These include:
- Generating summaries, microsites, and landing pages based on video data analysis.
- Enabling the easy creation of 3D printed objects from 3D models.
- Answering specific questions from unstructured data access and analysis, using an external system such as a video library.
- Empowering a personalized AI agent through calendar access.
- Coordinating efforts between multiple AI agents for more in-depth processing.
- Drawing immediate takeaways from meeting recordings or other long videos.
- Powering chat-based data analysis by combining chatbots with multiple databases.
- Automating agentic workflow execution for everyday efficiency gains.
Open-source MCP servers exist to help with key functions, including:
- Efficient web content fetching and conversion for LLM use.
- Sequential thinking for GenAI-based problem solving.
- Automatic time and time zone conversion.
- Git repository access.
- Persistent memory powered by knowledge graphs.
- Secure file operations and access controls.
Matching a server with a problem or use case allows developers to create LLM-powered tools that meet needs directly and more efficiently than would be possible without MCP integration.
Add Modern Intelligence to Your Video Management
As creating GenAI tools based on large language models becomes a key priority for companies across industries, decisions like MCP adoption can help this process remain efficient and deliver real value. The Vbrick MCP server securely connects video data from Vbrick EVP to AI systems like Copilot, ChatGPT, Claude, ServiceNow Now Assist, Agentforce, and others, enabling organizations to use video data as context to answer questions, extract insights, and automate work.
AI integration should be a way to provide a powerful return on investment for your business, rather than to simply keep up with a trend. Video management and analysis, enabled by multimodal AI solutions, can provide the value your organization is looking for. Video management platforms like Vbrick, with integrated AI functionality, enable this type of interaction, turning video content from a difficult-to-use, unstructured resource into vital AI app fuel for knowledge and overall progress.Â
Ready to see how you can harness the power of AI to manage video libraries and feed AI agents? Book a demo today!Â