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MCP-Bridge
SecurityAuto-generatedScore: 28

Airport & City Search MCP Server

The Airport & City Search API, provided by the global travel technology leader Amadeus, is a foundational data service designed to deliver precise, real-time reference data on airports, cities, and related geographic locations worldwide.

Quick Start Summary

The Airport & City Search MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the Airport & City Search API through natural language. It exposes 2 API endpoints as callable tools, such as Returns a list of airports and cities matching a given keyword., Returns a specific airports or cities based on its id.. No authentication is required — setup takes approximately 30 seconds. The server uses STDIO transport and can be installed by running npx -y @mcp/amadeus-com-amadeus-airport-city-search. This integration is sourced from the auto Airport & City Search OpenAPI specification (v1.2.3) and has a quality score of 28/99 (fair documentation coverage).

2Endpointstools mapped
NoneAuthopen access
28/99Qualityfair
~30 secSetupno auth
View Hosted Config URLClaude Desktop Setup Guide

Server Details

Category
Security
Authentication
None
Endpoints
2 operations
Transport
STDIO
Spec Version
v1.2.3
Install Command
npx -y @mcp/amadeus-com-amadeus-airport-city-search

Environment Variables

AIRPORT___CITY_SEARCH_API_KEY

Example: your_airport___city_search_api_key

Top Endpoints

GET
/reference-data/locations

Returns a list of airports and cities matching a given keyword.

GET
/reference-data/locations/{locationId}

Returns a specific airports or cities based on its id.

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📖 Detailed MCP Integration Guide

A technical breakdown of capabilities, agent workflows, and security/configuration best practices.

⚡Capabilities & Use Cases
The Airport & City Search API, provided by the global travel technology leader Amadeus, is a foundational data service designed to deliver precise, real-time reference data on airports, cities, and related geographic locations worldwide. Its core capability lies in transforming partial, natural-language, or contextual inputs—such as a city name, partial airport name, IATA code, or even geographic coordinates—into structured, machine-readable location identifiers. This service is indispensable for any travel, logistics, or aviation application that requires unambiguous location resolution. For enterprise systems, it powers flight search engines, booking platforms, and itinerary builders by ensuring correct airport selection, especially in complex metropolitan areas with multiple airports. For consumer applications, it enables intuitive autocomplete features and location-aware services, allowing users to quickly and accurately specify travel points. The API supports both broad discovery via the GET /reference-data/locations endpoint and detailed retrieval of a specific location's full profile using GET /reference-data/locations/{locationId}, including attributes like timezone, country code, and associated city information.
🤖AI Agent Value
When exposed as tools to an AI coding assistant via the Model Context Protocol (MCP), this API acquires transformative value, turning a static code generator into a dynamic, context-aware development partner. The AI agent gains the ability to ground its code generation and technical guidance in real-world, up-to-date travel data. This mitigates the risk of the model hallucinating outdated or incorrect IATA codes, time zones, or geographic relationships. For a developer building a travel feature, this means the AI can instantly validate location inputs, suggest canonical names, or programmatically resolve user-friendly text into the precise identifiers required by backend systems. The integration effectively reduces development friction, accelerates prototyping, and elevates the reliability of the AI's output by anchoring it to authoritative data, making the assistant significantly more powerful for tasks involving geographic or travel-related logic.
💬Example Workflows
In practical terms, a developer can instruct the AI agent to perform a variety of dynamic, data-driven tasks. For example, a developer could command, "Query the Airport & City Search API to find all major airports in Japan and generate a TypeScript enum with their IATA codes and names," automating the creation of a type-safe constant file. Another instruction might be, "Update our React booking form component to use the MCP tool to validate that a user's entered city exists and suggest the corresponding airport code," enabling the AI to write the exact validation and autocomplete logic. A more complex workflow could involve, "Analyze this dataset of flight search queries, use the API to identify ambiguous location mentions, and write a script to map them to precise airport codes," allowing the AI to act as a data processing and enrichment agent. This transforms the AI from a mere code autocompleter into an active participant in building and refining data-aware features.
🛡️Security & Auth
Critical to the secure and effective use of this API is the authentication requirement. Despite the initial description noting "None," integration requires an OAuth 2.0 access token, as referenced in the linked Authorization Guide. Developers must securely generate and store this token, implementing it as a bearer token in API request headers. Security best practices mandate the principle of least privilege, ensuring the token is issued with only the scopes necessary for location lookup and is never embedded in client-side code or version control. When configuring the MCP server, environment-specific tokens for development and production should be managed via secure environment variables or a secrets manager. It is also essential to use the test environment, which is based on a production subset, for development and validation to avoid unnecessary API calls and costs. Proper error handling for scenarios like rate limiting or authentication failures must be incorporated to ensure application resilience.

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