Travel Itinerary MCP Server

Overview

The Travel Itinerary MCP server is a purpose‑built backend that transforms static travel data into dynamic, AI‑driven itinerary recommendations. It solves the common pain point of piecing together travel plans by providing a single, well‑structured API that exposes hotel stays, transfers, activities, and day‑by‑day schedules. Developers can tap into this service to let AI assistants generate personalized itineraries, validate travel plans, or surface recommendations based on user preferences such as the number of nights.

At its core, the server is built with FastAPI for high‑performance request handling and SQLAlchemy to model relational data in a SQLite database. The API offers endpoints for creating, retrieving, and updating trip itineraries while also exposing a special recommendation endpoint that runs MCP logic. This logic evaluates the length of a stay and suggests optimal itineraries, drawing from pre‑seeded data for popular destinations like Phuket and Krabi. The result is a lightweight, self‑contained service that can be deployed behind any AI assistant without the need for complex orchestration.

Key capabilities include:

• Structured itinerary CRUD – Create, read, update, and delete trip plans with clear day‑wise segmentation.
• MCP‑enabled recommendations – A dedicated endpoint that applies model context rules to generate suggestions tailored to the user’s travel duration.
• Rich data modeling – Hotels, transfers, activities, and schedules are represented with full relational integrity, ensuring consistency across the itinerary lifecycle.
• Seeded sample data – Ready‑to‑use datasets for Phuket and Krabi accelerate prototyping and testing.

Typical use cases span from chatbots that help users book a week‑long vacation to internal tools that recommend activities for tour operators. An AI assistant can query the recommendation endpoint, receive a JSON itinerary, and then present it conversationally to the user. Because the server is MCP‑compliant, the assistant can seamlessly pass context such as user preferences or travel constraints, allowing for truly personalized planning.

What sets this server apart is its tight coupling of a conventional REST API with MCP logic. Developers can treat the recommendation endpoint as any other tool while benefiting from AI‑centric context handling, making it an ideal bridge between traditional backend services and modern conversational agents.