Smarter AV Starts Here: Why the Model Context Protocol (MCP) Matters

Agentic AI is about to fundamentally change the way we interact with technology. They won’t just be generating suggestions – they’ll be taking real actions on our behalf. Companies like Microsoft, ServiceNow, and Salesforce are already moving in this direction, enabling their AI agents to control calendars, devices, workflows, and even enterprise systems. 

These aren’t just smarter assistants – this is infrastructure that allows AI to directly communicate with systems, take initiative, and handle real-world tasks at scale. It’s a fundamental change in how systems are accessed and controlled. Just like humans interact with systems through user interfaces, AI agents can now communicate directly and take action autonomously.

But to do that reliably across different systems and vendors, they need a common language. That’s where the Model Context Protocol (MCP) comes in. Think of it like USB-C – a universal connector, but for AI. Just like USB-C made it easier to plug anything into anything, MCP makes it easier for AI agents to plug into the digital tools we rely on. Instead of building fragile, one-off integrations, developers can define what tools can do in a consistent way – and AI takes it from there.

We’re already starting to see this future take shape. If you’ve seen Amazon’s new Alexa Plus, you’ve already seen glimpses of this future. Now imagine an AI agent detecting an audio dropout in a corporate boardroom, rebooting the DSP, and alerting IT – all before the meeting starts. Or monitoring camera feeds across dozens of classrooms, adjusting settings for lighting or occupancy, and escalating issues only when human attention is needed. 

These kinds of proactive, cross-platform actions are only possible when AI agents have a simple, scalable way to communicate with other systems – and that’s exactly what MCP enables. In this blog, we’ll take a deep dive into what MCP is and how it will transform the way the AV industry operates.

What is the Model Context Protocol (MCP)?  

The Model Context Protocol (MCP) is a framework developed by Anthropic, and adopted by OpenAI and other large AI and technology players in the market. MCP gives AI agents a standardized way to interact with external systems. Instead of relying on custom APIs or rigid integrations, MCP lets developers describe what external systems - like AV devices, APIs, and management utilities - can do using clear, structured schemas. This makes it easier for AI models to understand those capabilities and determine how to use them safely and effectively. For industries like AV - where systems are often complex, vendor-specific, and poorly integrated - this approach is especially valuable. 

To understand the impact, consider how people use ChatGPT today. It can help plan a trip to Italy – but with MCP, that same agent could go a step further: booking the flights, reserving the hotel, and syncing everything to your calendar. That’s the power of giving AI a standardized, machine-readable way to act. Now apply that to AV – where agents could restart a malfunctioning device, reset audio levels, or apply a known fix across multiple rooms – all automatically, and without relying on a human to click through a dashboard.

The AV Industry: A Landscape Ripe for Change  

The shift toward more open, connected AV systems is already underway. Emerging industry standards and open APIs are encouraging vendors to build with interoperability in mind. MCP is the next logical step.

It’s a shift that’s badly needed. AV environments are still messy behind the scenes – proprietary protocols, one-off integrations, and tools that don’t talk to each other. MCP helps solve this. And our customers are already starting to ask for it. That’s why Xyte is embracing this vision with the Xyte MCP Server, which will give OEMs and integrators a way to expose device capabilities in a secure, AI-ready format.

Why MCP Could Be a Game-Changer for AV  

Enterprise customers are deploying more technology than ever – and they’re under pressure to manage it at scale. Rather than growing support teams, many are adopting AI-driven platforms like ServiceNow and Salesforce to automate everyday tasks and reduce manual overhead.

For AI-driven platforms to work effectively, they need seamless access to the underlying AV systems — and that’s exactly what MCP enables. That means AV vendors, consultants, integrators and other service providers will increasingly be asked whether their products can support AI agents – not in the future, but now.

This is why MCP is a game-changer in AV. It’s designed exactly for this kind of interaction. It defines what a system can do in a structured, machine-readable way, so agents can understand and act on those capabilities without relying on custom integrations. In environments where hardware, software, and control systems vary from room to room, this kind of consistency is critical. MCP also supports clear guardrails, allowing actions to be controlled, tracked, and rolled back if needed.

Real-World Applications for Agentic AI in AV  

What does this look like in practice? When AV systems expose their capabilities in a standardized, structured way, MCP-enabled AI agents can take on a range of meaningful, hands-on tasks - like the ones below.

• Autonomous troubleshooting and fixes  

An AI agent using MCP can detect issues like a device going offline or a misconfigured setting. Instead of waiting for a technician, it can query system status and apply a fix - such as rebooting the device or restoring default settings - without user input. This reduces downtime and service tickets.

• Dynamic environmental adaptation  

With access to sensor data and system controls, an AI agent can adjust audio levels, display brightness, or camera framing based on room occupancy, lighting conditions, or time of day. MCP allows these actions to happen safely and consistently across different vendor equipment.

• Predictive maintenance and failure prevention  

MCP agents can monitor telemetry from devices and identify early signs of wear or failure - like abnormal heat levels or declining signal quality. This enables proactive repairs or replacements before issues impact users.

• Security monitoring and response  

AI tools can analyze network traffic for suspicious patterns or unexpected device behavior. If it detects tampering or compromise, it can isolate affected components and escalate alerts, all within a structured, auditable framework defined by MCP.

• Self-healing RMM  

Instead of just reporting problems, an MCP-aware system can resolve minor issues on its own. For example, if a firmware update causes a device to misbehave, the AI can roll it back or reapply stable settings while still notifying administrators.

MCP and Xyte: Building Smarter Foundations for AV

MCP fits naturally with where the AV industry is headed – toward openness, standardization, and smarter, more connected infrastructure. Platforms like Xyte support this shift by giving manufacturers consistent, cloud-based tools to expose device capabilities. That makes it easier to manage systems remotely and integrate them into broader digital ecosystems.

MCP takes this further by allowing AI agents to interpret and act on those capabilities. Instead of developing custom code for each device, developers define functions once, and agents can apply them across varied environments – reliably, securely, and at scale.

This combination does more than streamline integration. It lays the foundation for AV systems that can adapt, self-correct, and operate with greater autonomy – without sacrificing oversight, accountability, or cross-vendor compatibility. For AV teams under pressure to do more with less, that’s a critical shift.