The Simple Explanation

Imagine a universal adapter on your hotel nightstand. One side accepts any plug, the other fits your local socket. MCP works similarly, but for software.

On one end: Your AI assistant (like ChatGPT or Claude etc)

On the other end: Your hotel systems (PMS, POS, revenue tools, maintenance platforms)

In the middle: MCP, the neutral layer that lets them communicate

Before MCP, AI could answer questions and generate text, but it couldn’t actually pull a reservation, update a booking, or trigger a maintenance request. With MCP, AI has the potential to stop being a spectator and become a participant.

MCP Is Now an Open, Vendor-Neutral Standard

In December 2025, Anthropic donated MCP to the new Agentic AI Foundation (AAIF), a directed fund under the Linux Foundation. This move is intended to keep MCP open, vendor-neutral, and community-governed.

The foundation was co-founded by Anthropic, Block, and OpenAI, with support from Amazon Web Services, Google, Microsoft, Cloudflare, and Bloomberg. MCP joins two other founding projects: Block’s Goose and OpenAI’s AGENTS.md.

This governance structure means no single vendor controls MCP’s future. Developers and enterprises can invest confidently in standard, knowing it will remain open and interoperable. Project maintainers report over 97 million monthly SDK downloads, about 10,000 active servers, and first-class client support across ChatGPT, Claude, Cursor, Gemini, Microsoft Copilot, and Visual Studio Code.

Why This Matters for Hotels

Hotels today run on a patchwork of systems. One handles bookings, another manages payments, another tracks housekeeping. These systems technically “talk” to each other through APIs, but only behind the scenes, and rarely in ways that help your front-line staff.

The result? Your team logs into three different platforms just to answer one guest request. Every new tool requires expensive custom integrations. Replacing a vendor means months of IT projects.

MCP aims to change this by creating one standard language. Each system still must build and maintain its own MCP server or adapter, but once connected, any compliant AI agent can access it, understand what it can do, and take action, without bespoke AI-specific integrations for every connection.

The USB-C Analogy

Industry commentators often call MCP the “USB-C port for AI,” and the metaphor is useful for understanding the concept.

Before USB-C, every device had its own charger and cable. Travel meant carrying a bag of adapters. USB-C created one standard that works everywhere.

MCP aspires to do the same for hotel technology. Instead of building custom connections between every AI tool and every hotel system, MCP provides one universal plug. Connect once, and any compliant AI can work with any compliant system. This is the vision, though real-world implementation still requires work from each vendor to expose their systems properly.

What AI Could Do With MCP

Here are examples of how MCP could transform daily operations when systems expose the right actions, AI agents are granted proper permissions, and human approval rules are defined where required:

Front Desk Check-In

Before: Staff open three systems to verify ID, process payment, and assign a room.

With MCP: An AI agent can orchestrate all three tasks in a single flow, with staff approval at key steps.

Restaurant Inventory

Before: Manual counts, printed reports, and emailed supplier orders.

With MCP: The AI can read POS data, check thresholds, and prepare supplier orders for review.

Night Audit (Emerging Use Case)

Before: Download reports, upload files, reconcile manually.

With MCP: The AI could manage end-of-day reconciliation and journal entries, pending human review. This is an early pilot scenario, not yet common in production.

Maintenance Requests

Before: Housekeeping texts engineering. Tasks get missed. Room status updates fall behind.

With MCP: The AI can route tasks in real time, schedule them, and update room status automatically within defined rules.

Complex Reservations

Before: A guest request for rescheduling, airport transfers, and connecting rooms requires jumping between multiple systems.

With MCP: The AI can initiate and coordinate changes in a single conversation, with confirmation steps as needed.

Revenue Analysis (Emerging Use Case)

Before: Export data from PMS, pull forecasts from RMS, check catering requirements, verify VIPs in CRM, compile manually into a presentation. Takes hours.

With MCP: An AI agent could pull bookings, overlay forecasts, flag VIPs, identify risks, and generate a draft report in minutes. This represents the near-future potential, with early pilots underway at select properties.

MCP Is Not AI, It Enables AI

This distinction matters. MCP does not think, decide, or learn. It does not generate content or hold conversations with guests. It does not replace staff.

MCP is the protocol, the structure, the grammar. It tells AI agents what exists within your hotel tech stack, what actions can be taken, and how to take them.

AI agents (like ChatGPT or Claude) are the ones that plan, reason, and execute. But without structured access to your tools and data, even the smartest AI is just a spectator. It might sound impressive, but it cannot actually do anything.

MCP turns intention into action.

Who Is Exploring MCP?

Some hotel technology providers are actively experimenting with MCP and agent-friendly architectures. Most implementations are still in early stages, pilots, or concept demonstrations rather than full production deployments.

Apaleo has demonstrated agent-compatible workflows and MCP-style experiments, showing how AI agents like Claude could directly interact with PMS functions such as modifying bookings or updating housekeeping schedules.

Mews has strong APIs and AI partnerships. Concept demos have shown workflows where an AI retrieves PMS data and formats it into external tools, though this requires multiple connected systems beyond just Mews.

Outside hospitality, major companies are investing heavily in AI-driven automation:

Amadeus is exploring agent-driven automation for flights and service disruptions, though primarily through proprietary frameworks rather than confirmed MCP adoption.

Block (formerly Square) uses AI-driven automation at scale for fraud detection and payment processing, and contributed its goose project to the Agentic AI Foundation alongside MCP.

What This Means for Your Tech Stack

MCP becomes a useful indicator for your current systems. If your tools are cloud-native and well-documented, you may not need to rip and replace to be AI-ready. You just need to connect.

If your systems are legacy, closed, or poorly documented, MCP exposes that gap. It signals readiness (or lack of readiness) for agent-based systems and the direction the industry is heading.

The vendors who embrace open standards give you flexibility. The vendors who resist them are more likely to create lock-in.

The Shift: From Interface to Infrastructure

Until now, most “AI” in hospitality has been confined to shallow use cases: chatbots, recommendation engines, dashboards. Useful sometimes, but not transformational.

The reason is simple: intelligence without access is just performance. A brilliant AI that cannot pull a reservation or update a status is just another layer of abstraction, another system to manage rather than a system that manages for you.

MCP represents a shift from AI as an interface (something you interact with) to AI as infrastructure (something that works inside your operations). When that transition matures, your team can spend less time wrestling with fragmented screens and more time on what machines cannot replicate: empathy, creativity, intuition, the real essence of hospitality.

The Bottom Line

MCP is not the story. It is the grammar behind the story. It aims to remove friction between your staff and your systems. When it works well, guests will not see the technology. They will notice the effect: fewer delays, smoother transitions, and moments that feel personal instead of pre-programmed.

For hotels willing to explore it, MCP represents the direction of travel for hospitality technology, a blueprint for how AI and operations could work together. The full vision is still emerging, but the foundations are being built now.