Introduction: From Interface to Infrastructure
The moment AI agents can plug into the systems your hotel already runs on (PMS, POS, RMS, CRM, etc.), they stop being expensive toys with fancy language models and start behaving like digital coworkers. And let’s be clear on the semantics here: not “assistants.” Real (well, kinda) colleagues, capable of executing actual operational work: updating bookings, managing inventory, triggering maintenance, orchestrating systems and processes. This is the fundamental shift we’ll be witnessing over the next few months/years: the move from artificial intelligence as an interface to artificial intelligence as an infrastructure.
Until now, most so-called “AI” in hospitality has been confined to shallow use cases, like chatbots, recommendation engines, and flashy BI dashboards. Useful? Sometimes. Transformational? Nah… And the reason is simple: intelligence, whether human or artificial, without access is just performance. You can have the most advanced system in the world, but if it can’t interact with your day-to-day ops (pull a reservation, update a status, execute a workflow), then it’s just another layer of abstraction. Another system to manage, rather than a system that manages for you. This is where the Model Context Protocol (MCP, for short) comes in. MCP is a protocol. A shared language. A neutral standard that can (finally) give AI systems the ability to operate inside your tech stack, and not around it. And when that happens, everything changes.
This article is a field guide to that change.
What Is MCP, Anyway?
The Model Context Protocol (MCP) is an open standard introduced by Anthropic on November 25, 2024 (see the official announcement here). You can think of it as a lingua franca between LLMs/AI agents and software systems, a structured, machine-readable grammar that allows large models not just to talk about your tools, but to talk to them. Technically speaking, it is JSON in and JSON out.
Every system that supports MCP exposes what it can do (its capabilities, data models, and commands) in a consistent, standardized format. No more praying to the API documentation Gods (Amen!). Practically speaking, MCP is the missing connective tissue, the layer that binds the AI brain to the operational nervous system of the hotel. Or, to put it in Tolkien’s terms, the one protocol to rule them all. And while the travel industry is still busy fighting over whose sandbox is bigger, Anthropic quietly open-sourced the blueprint to a post-API world. A world where agents do not just analyze data, but execute on it, across systems, natively and in real time.
In short, MCP is not a feature. It is an operating principle. And its arrival might mark the first serious attempt to dismantle the closed, feudal structure of hotel tech integration.
For the Geeks: What MCP Looks Like in Practice
If you’re wondering what this actually looks like under the hood, here’s a simplified example of an MCP interaction between an AI agent and a PMS. In this case, the agent is modifying a booking by extending the guest’s checkout time.
Request:
{ “tool_use”: { “tool_name”: “hotel_pms”, “action”: “modify_booking”, “parameters”: { “booking_id”: “BKG123456”, “new_checkout_time”: “2025-06-15T14:00:00Z” } } }
Response:
{ “tool_response”: { “tool_name”: “hotel_pms”, “action”: “modify_booking”, “status”: “success”, “updated_booking”: { “booking_id”: “BKG123456”, “guest_name”: “Simone Puorto”, “room_number”: “504”, “checkout_time”: “2025-06-15T14:00:00Z” } } }
Looks too simple? That’s the point. No vendor-specific quirks, no obscure API docs, no hardcoded integrations. Just one agent speaking to one tool in a universal, standardized format. That said (and tech folks, you might want to brace yourselves), we are, of course, deliberately oversimplifying here to keep things readable for non-technical audiences (in a real scenario, you’d also expect to see metadata like session or request IDs, timestamps, agent credentials, and contextual context. There would also be a manifest file on the PMS side, listing supported actions, accepted parameters, and expected outputs). But even with that scaffolding, the core idea remains: MCP flattens the landscape. Every tool looks the same to the agent, so that every function is discoverable and every response predictable. It’s not just easier. It’s interoperable by design.
The USB-C port for AI
Think of that universal adapter on your hotel nightstand. One side takes any plug, the other fits your socket. MCP works similarly, but for software. Some call it the USB-C port for AI, and we think it’s a fitting example. On one end, you have your AI agent/LLM. On the other hand, your operational tools (PMS, POS, RMS, energy systems, maintenance platforms, or even Google Sheets, if needed). MCP is the neutral layer in between. The AI can query, act on, and understand any connected system instantly because everything follows the same standardized structure.
So, let’s ground it in a real-world scenario: imagine your revenue manager needs to prepare next week’s group booking analysis. This involves exporting data from the PMS, pulling forecasts from the RMS, adding catering requirements, verifying possible VIPs in the CRM, and manually compiling all the information into a presentation deck. It takes hours, involves constant context-switching, and almost always introduces human errors. With MCP, you just say, “Prepare next week’s group analysis,” and the AI pulls group bookings, overlays forecasts, integrates logistics, flags VIPs, identifies overbooking risks, and generates a clean, ready-to-use report. Done in under two minutes.
Why APIs Aren’t Plug-and-Play. They’re Plug-and-Pray (And What MCP Can Actually Fix)
Hotels today operate on a complex patchwork of systems, each connected through fragile application programming interfaces. One tool handles bookings, another takes care of payments, another manages housekeeping, and so on. Sure, these systems, technically, “talk” to each other, but only behind the scenes and rarely in ways that support real human workflows. That’s because APIs were never designed for front-line staff. They speak machine, not operations. So even when your systems are integrated, your team still has to log into three different platforms just to answer a guest request.
And it gets worse. Every new integration is a bespoke project. Want to replace one tool or bring in that promising new startup? Get ready for vendor calls, (expensive) integration roadmaps, legal reviews, and probably a non-disclosure agreement. Many vendors, especially legacy PMS providers, do not sell software anymore. They sell lock-in. And no one in the industry is really pretending otherwise. MCP, at least in principle, breaks that cycle. Each system needs only one adapter. Once connected, any compliant AI agent can access it, understand its capabilities, and act accordingly. No extra layers. No dashboard overload. Your team talks to the AI, and the AI talks to everything else.
Here are some examples of how MCP can transform daily ops:
Front Desk Check-in
Before: Staff open three systems to verify ID, process payment, and assign a room.
With MCP: One AI prompt handles all three tasks in a single flow.
Restaurant Inventory
Before: Manual counts, printed reports, and emailed supplier orders.
With MCP: The AI reads POS data, evaluates thresholds, and prepares supplier orders automatically.
Night Audit
Before: Download reports, upload files, and reconcile manually.
With MCP: The AI manages end-of-day reconciliation and journal entries without intervention.
Kitchen Prep
Before: Staff estimates prep needs based on guesswork or basic occupancy data.
With MCP: The AI generates prep lists based on live booking forecasts and expected covers.
Maintenance
Before: Housekeeping texts engineering. Tasks get missed, and room status updates fall behind.
With MCP: The AI routes tasks in real time, schedules them, and updates room status automatically.
Reservations
Before: A simple request requires jumping between systems.
With MCP: The AI handles even complex scenarios, rescheduling, airport transfers, connecting rooms, special requests, all in a single conversation.
Concierge
Before: Staff make calls, check availability, and jot notes.
With MCP: The AI cross-references guest preferences, real-time availability, and external APIs to suggest and book instantly.
Early Adopters Already Building on MCP
Some suppliers aren’t waiting for the industry to agree on standards. They’re already building on top of MCP, and proving that this isn’t theoretical. It’s happening.
Apaleo is one of the (if not the) first movers. The German-born cloud PMS built an entire Agent Hub, a dedicated environment where AI agents can operate autonomously across hotel operations. Agents like Claude can now directly interact with Apaleo through standardized MCP calls. That means modifying bookings, confirming late check-outs, updating housekeeping schedules, and sending guest confirmations, all without human hand-holding, and more importantly, without a single line of custom integration code.
Mews has also entered the MCP ecosystem via community-built connectors. You can now ask ChatGPT something like “Create a spreadsheet of tomorrow’s VIP arrivals”, and the AI will retrieve the data from the PMS, format it into Google Sheets, and share it with your team, instantly.
And these aren’t isolated demos. They are functional, real-world implementations that show what MCP unlocks when the underlying stack is structured correctly. The lesson is clear: if your systems are cloud-native and well-documented, you don’t need to rip and replace to be AI-ready. You just need to connect. And if your systems aren’t? Then MCP becomes a litmus test. Not just for compatibility, but for whether your tech stack belongs to the future (or the past).
MCP vs. AI Agents: Know the Difference
Let’s be clear: MCP is NO artificial intelligence. It doesn’t think, decide, or learn. It doesn’t generate content or hold conversations with guests. And it doesn’t (yet) replace staff. What it does is more foundational.
MCP is the protocol. The structure. It’s the standard that tells AI agents what exists within your hotel tech stack, what actions can be taken, and how to take them. Think of it as the semantic infrastructure beneath the intelligence layer. AI agents/LLMs (Claude, ChatGPT, or domain-specific models trained on hotel operations) are the ones that plan, reason, and execute. But without structured access to tools and data, even the most advanced model is just a smart spectator. It might sound impressive, and it might even be helpful, but it can’t really do much. MCP is what turns that intention into action. It’s the missing grammar that transforms passive reasoning into operational output. That’s the real distinction: AI agents are the digital workers we referenced in the introduction, while MCP is the environment that makes their work possible. This opens the door to a fundamental shift in how human and machine decision-making coexist. It allows us to move from humans-in-the-loop (where AI assists but humans retain full control), to humans-on-the-loop (where AI operates autonomously within set boundaries), and, in some cases, toward humans-out-of-the-loop (where the AI acts entirely on its own once goals and constraints are defined, with no manual input).
Together, these models reflect a deeper truth: the future of hospitality isn’t just about automation, but rather orchestration. And MCP is the first credible tool that enables us to design systems around intent and outcome, rather than vendor limitations or integration constraints.
Other Industries Are Already There
While hospitality is only beginning to explore MCP’s potential now, other industries have already moved into implementation mode, and the results are telling.
In aviation, Amadeus is leveraging MCP to allow AI agents to book flights and manage service disruptions without requiring human intervention. What once required specialized interfaces and manual workflows is now handled through a unified layer of logic.
In finance, Block (formerly Square) has adopted MCP to power fraud detection and payment processing at machine speed. Instead of building custom integrations for every internal tool, the AI agent simply queries standardized functions exposed through the protocol, turning what used to be months of engineering work into seconds of automation.
In software development, Replit has integrated MCP into its coding environment. This allows models like Claude to read, test, and modify code files autonomously, without the need for separate plugins or language-specific wrappers.
The Future: Less Friction, More Focus
MCP doesn’t replace your staff or your systems. It removes the friction between them. When it’s working, guests won’t see the technology. It’s like a hospitality ninja: operating by stealth. What they’ll notice is the effect: fewer delays, smoother transitions, and moments that feel personal instead of pre-programmed. And your team? They’ll stop wrestling with fragmented interfaces, jumping between screens, and compensating for tech that was never built with them in mind. They’ll get back to what machines can’t replicate: empathy, creativity, intuition. The real stuff. The essence of hospitality.
MCP may not be the story. But it’s the grammar behind it. And for those like us, who work with words on a daily basis, grammar does matter.
And, if it’s true that the best way to predict the future is to invent it,” then MCP is not just a prediction. It’s a blueprint. With it, we might just stop imagining what’s next.
And start building it.
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