MCP Use Case Levels in Travel & E-Commerce: From Vision to Execution

MCP (Model Context Protocol) is a framework that allows AI agents (like ChatGPT) to interact with external tools, perform real-world tasks, and deliver outcomes in specific domains such as e-commerce, travel, and healthcare. But to effectively implement MCP, it’s crucial to define its use cases across different levels — from high-level strategic actions to low-level technical interactions.

Let’s explore what these MCP use case levels mean and how they apply in real-world industries.

What is a Use Case?

A use case is a structured description of how users (actors) interact with a system to achieve a specific goal. Each use case outlines a sequence of actions and system responses.

Understanding Use Case Levels in MCP

High-Level Use Cases (Cloud/Kite View)

• Describe big-picture objectives
• Involve strategic goals like “Automate Product Discovery” or “Optimize Travel Planning.”
• Don’t mention system steps, only goals

Mid-Level Use Cases (Sea View)

• Describe system-user interactions
• Focus on user journeys: what users do and what system does in response

Low-Level Use Cases (Fish/Clam View)

• Include step-by-step, detailed flows
• Include error handling, validations, backend APIs, third-party interactions

E-Commerce Industry Use Cases with MCP

High-Level Use Case: Intelligent Product Management

Goal: Automate real-time product listing updates, pricing, and stock level management using AI.

Mid-Level Use Case: Personalized Buyer Experience

Scenario: A user visits the site; the AI recommends products based on user history, offers personalized bundles, and auto-applies available coupons.

Actors: User, AI agent, Product API, Coupon API

Flow:

1. User logs in.
2. AI fetches behavior data.
3. Personalized recommendations shown.
4. Cart optimized with offers.

Low-Level Use Case: Automated Order Fulfillment

Actors: Buyer, AI Agent, Inventory System, Payment Gateway, Shipping Partner

Steps:

1. User confirms order.
2. AI confirms stock via Inventory API.
3. AI triggers payment using a secure API.
4. AI generates shipping labels.
5. The system updates order status.

Travel Industry Use Cases with MCP

High-Level Use Case: AI-Based Travel Planning

Goal: The AI agent curates the best travel route, accommodation, and sightseeing plan based on the user’s budget and preferences.

Mid-Level Use Case: Dynamic Booking Management

Scenario: A user requests to book a trip. The AI handles searching, filtering, and confirming bookings across multiple APIs.

Steps:

1. The user provides the destination and date.
2. AI fetches results from airline/hotel APIs.
3. Best options shown to user.
4. Booking completed with payment.

Low-Level Use Case: Real-Time Refund & Support

Flow:

1. The user raises a cancellation request.
2. AI verifies refund policies from the airline system.
3. Initiates refund via payment API.
4. Sends the user a confirmation SMS/email.

Benefits of Structuring Use Cases in Levels

1. Improved Clarity Across Teams

High-level use cases help business stakeholders focus on strategic goals without getting lost in technical details.

2. Efficient Role-Based Communication

Each level of use case (high, mid, low) serves a specific audience: executives, functional teams, and developers respectively.

3. Modular and Scalable Design

Breaking down use cases makes it easier to develop features in modular blocks, which improves scalability and system maintainability.

4. Streamlined Development & QA

Low-level use cases offer detailed inputs, validations, and responses, reducing ambiguity for developers and testers.

5. Better Traceability & Documentation

Organizing use cases into levels creates a clear audit trail from business objectives to code-level implementation.

How MCP Enhances Use Cases?

Context Awareness

MCP enables AI agents to remember previous interactions, making them capable of more intelligent and personalized responses.

Tool Integration Capability

Through MCP, AI agents can connect with various systems (like CRMs, CMS, payment gateways), enabling automated, end-to-end workflows.

Multi-Modal Processing

MCP empowers agents to process different types of inputs—text, image, data, APIs—making the use cases richer and more adaptable.

Real-Time Interactivity

It allows AI agents to respond to real-time data and events, helping businesses implement highly dynamic use cases.

Scalable Automation

By managing tasks across tools and systems, MCP ensures AI-driven automation scales smoothly without manual interventions.

Conclusion

Implementing MCP with clearly defined high-, mid-, and low-level use cases ensures a scalable, modular, and user-centric system. Whether its helping users shop smarter or plan better travel experiences, MCP unlocks the full potential of AI in action.