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Travel Itinerary Backend System with MCP

@aryanranderiya

Repository

About Travel Itinerary Backend System with MCP

FastAPI-based travel itinerary system with MCP integration for AI-powered discovery, filtering, and creation of travel plans via natural language.

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "itinerary-mcp-server": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

Tools

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Overview

What is Travel Itinerary Backend System with MCP?

A FastAPI-based backend for managing travel itineraries, implementing the Model Context Protocol (MCP) for AI assistant integration. It provides RESTful endpoints to create and view trip itineraries, with pre-populated seed data for Phuket and Krabi regions. Designed for developers and AI tool users who need a structured travel planning backend.

How to use Travel Itinerary Backend System with MCP?

Install Python 3.8+, clone the repository, create a virtual environment with uv venv and install dependencies with uv sync. Run the server in development mode using fastapi dev or production mode with fastapi start. The MCP integration is built-in – connect any MCP-compatible client (e.g., GitHub Copilot, Claude Desktop) to the SSE endpoint at http://localhost:8000/mcp. No additional configuration is needed.

Key features of Travel Itinerary Backend System with MCP

  • SQLAlchemy models for itineraries, accommodations, transfers, and activities
  • RESTful API to create and view trip itineraries
  • Pre-populated seed data for Phuket and Krabi regions
  • Sample itineraries ranging from 2 to 8 nights
  • Full Model Context Protocol support via fastapi_mcp library
  • Server-Sent Events (SSE) endpoint at /mcp

Use cases of Travel Itinerary Backend System with MCP

  • AI assistants retrieve and display all recommended itineraries for a destination
  • Users create new multi-day itineraries with specific hotels and activities via natural language
  • Developers integrate travel planning capabilities into AI-powered tools and chatbots
  • Rapid prototyping of itinerary management with realistic seed data

FAQ from Travel Itinerary Backend System with MCP

What is MCP and how does this server use it?

The Model Context Protocol (MCP) enables AI assistants to understand and interact with the API directly. This server uses the fastapi_mcp library to expose API endpoints via an SSE endpoint at /mcp.

What are the system requirements?

Python 3.8 or higher, and the uv package manager for Python.

How do I start the server?

For development with auto-reload: fastapi dev. For production: fastapi start. The API is then available at http://localhost:8000.

Which MCP clients are supported?

Any MCP-compatible client, including GitHub Copilot, Claude Desktop, and other MCP-compatible assistants.

Does the server come with sample data?

Yes, the database is pre-populated with realistic seed data for the Phuket and Krabi regions, including sample itineraries of 2–8 nights.

Frequently asked questions

What is MCP and how does this server use it?

The Model Context Protocol (MCP) enables AI assistants to understand and interact with the API directly. This server uses the fastapi_mcp library to expose API endpoints via an SSE endpoint at `/mcp`.

What are the system requirements?

Python 3.8 or higher, and the uv package manager for Python.

How do I start the server?

For development with auto-reload: `fastapi dev`. For production: `fastapi start`. The API is then available at `http://localhost:8000`.

Which MCP clients are supported?

Any MCP-compatible client, including GitHub Copilot, Claude Desktop, and other MCP-compatible assistants.

Does the server come with sample data?

Yes, the database is pre-populated with realistic seed data for the Phuket and Krabi regions, including sample itineraries of 2–8 nights.

Comments

Basic information

Category

Media & Design

Transports

stdio

Author

aryanranderiya

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