What Is MCP and Why It Matters for Tourism Businesses

What MCP is, in one sentence

MCP (Model Context Protocol) is an open standard that lets an AI assistant (such as Claude, ChatGPT or Gemini) connect to external tools and data through a server that exposes actions with defined permissions. Put another way: it's the bridge that lets an AI assistant stop just "talking" and start "doing" things inside your business software, with clear rules about what it can touch and what it can't.

For a tour operator, an agency or a host, this isn't a minor technical detail. It's the difference between an AI assistant that only gives you ideas and one that can check your real catalog, create a booking or update an itinerary while you talk to it.

A simple analogy

Think of MCP as a universal plug. In the past, every electrical device needed its own type of connector and its own adapter. A plug standard solves that: any compatible device connects to the same outlet, without special cables or manual translation.

MCP does something similar between an AI assistant and a business's software. Instead of every system having to "learn" how to talk to every assistant separately, both sides use the same standard. The AI assistant connects to a product's MCP server, sees which actions are available (and with what permissions) and uses them when the conversation calls for it. Nobody has to build a different integration for every assistant that exists.

This matters especially for a tour operator without a development team. You don't need to hire anyone to "connect" your business to AI: if the software you use already exposes an MCP server, the connection is a matter of generating a URL and pasting it into your assistant.

How MCP works inside Skyline

At Skyline, the tourism software ecosystem, partners can operate their account from an AI assistant (Claude, ChatGPT, Gemini) via MCP. This isn't limited to a single product: it applies to how the ecosystem connects operational software with the AI assistants an operator already uses every day.

In practice, this means a tour operator in Cartagena who already uses Claude or ChatGPT to organize their day can ask it, within that same conversation, to check their catalog, look up a booking or build an itinerary. The assistant isn't "guessing" or improvising: it's using the actions exposed by the MCP server, with the business's real data.

This has three important implications for an operator:

1. You don't need a new app. You keep using the AI assistant you already have installed, without learning a different interface.
2. The AI operates with real data, not with what it "thinks" your catalog or availability looks like.
3. Permissions are defined, so the assistant can only do what the MCP server allows it to do, no more and no less.

Three concrete uses in tourism, no jargon

1. Checking the catalog from chat

A tour operator with three guides who sells hiking tours can ask their AI assistant, in plain language, what experiences are available for a group of six people next Saturday. The assistant checks that information through the connected MCP server and answers with real data, without the operator having to open a different panel or dig through a spreadsheet.

2. Creating or updating a booking by talking

Instead of filling out a form, a host can describe the details of a booking to their AI assistant (dates, number of people, experience) and have the assistant register it directly in the system. This is especially useful when the operator is already using AI for other tasks during the day and doesn't want to switch tools every time a new customer reaches out on WhatsApp or Instagram.

3. Building an itinerary while talking to the traveler

An agency in Medellín that builds custom trips can ask their AI assistant to build a day-by-day plan for a traveler, based on the real experiences available, and generate a link to share. The assistant doesn't invent activities or prices: it works on what the MCP server exposes, which in turn comes from the business's real catalog.

These three uses share something in common: the operator doesn't need to know what a server, an API or a protocol is. They only need to generate the connection once and then keep talking to their AI assistant as they already did, but now with the ability to act on their own business.

MCP versus other ways of connecting software

The table below compares, in general terms, how different types of tools connect to an AI assistant.

The advantage of an open standard like MCP is that the connection work is done once on the software side (in this case, on Skyline's side), and any compatible AI assistant can take advantage of it, without the operator having to manage separate integrations for every platform they use.

What you need to get started

You don't need to know how to code. All you need is an AI assistant compatible with MCP (such as Claude, ChatGPT or Gemini) and an active account on the Skyline product you want to operate by chat. The connection is generated from the product's Integrations section: you copy a URL and paste it into the assistant's settings.

Once connected, the assistant stops being just a place where you type questions and becomes an extension of your daily operation: it can check, create and update real information about your business, always within the permissions the MCP server defines.

What to do next

If you already use Claude, ChatGPT or Gemini to organize your operation, the next step is to generate your MCP connection from the Integrations section of the Skyline product you use and test it with a simple query, like asking the assistant to list your available experiences.

More about Skyline: skyln.co