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MCP-Bridge
SecurityAuto-generatedScore: 28

Location Score MCP Server

The Location Score API, provided by Amadeus for Developers, is a sophisticated analytical service designed to quantify and compare the appeal, business potential, or suitability of geographical areas based on a rich taxonomy of categories.

Quick Start Summary

The Location Score MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the Location Score API through natural language. It exposes 1 API endpoints as callable tools, such as GET category rated areas. 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-location-score. This integration is sourced from the auto Location Score OpenAPI specification (v1.0.2) and has a quality score of 28/99 (fair documentation coverage).

1Endpointstools mapped
NoneAuthopen access
28/99Qualityfair
~30 secSetupno auth
View Hosted Config URLClaude Desktop Setup Guide

Server Details

Category
Security
Authentication
None
Endpoints
1 operations
Transport
STDIO
Spec Version
v1.0.2
Install Command
npx -y @mcp/amadeus-com-amadeus-location-score

Environment Variables

LOCATION_SCORE_API_KEY

Example: your_location_score_api_key

Top Endpoints

GET
/location/analytics/category-rated-areas

GET category rated areas

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📖 Detailed MCP Integration Guide

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

⚡Capabilities & Use Cases
The Location Score API, provided by Amadeus for Developers, is a sophisticated analytical service designed to quantify and compare the appeal, business potential, or suitability of geographical areas based on a rich taxonomy of categories. At its core, the API, accessed via the GET /location/analytics/category-rated-areas endpoint, transforms raw location data into actionable intelligence by returning a computed "score" for specific areas (defined by geographic coordinates or IDs) relative to selected categories such as "tourist attractions," "restaurants," "shopping centers," or "public transport." This enables developers to move beyond simple point-of-interest searches to understand the *character* and *value* of a location programmatically. The primary use cases span across enterprise travel and corporate services—for instance, a travel management company could use location scores to recommend hotel districts with high business amenity density—or consumer-facing applications like real estate platforms seeking to highlight neighborhood vibrancy for prospective renters or retail chains performing site-selection analysis for new store openings.
🤖AI Agent Value
When exposed as a tool via the Model Context Protocol (MCP), this API gains profound utility for AI coding assistants, transforming them from code generators into context-aware location intelligence analysts. An AI like Claude Desktop or Cursor, integrated with this MCP server, can dynamically fetch and interpret spatial data to answer complex, natural-language queries that previously required manual database lookups or bespoke scripting. The value lies in bridging the gap between high-level developer intent and low-level API invocation. Instead of writing code to handle HTTP requests, parse JSON responses, and calculate comparisons, a developer can simply instruct the AI with a task, and the model will structure the appropriate API calls, interpret the scores, and present synthesized insights, significantly accelerating the prototyping and development of location-aware applications.
💬Example Workflows
In practice, a developer can leverage the AI agent to perform a variety of dynamic tasks. For example, the instruction "Analyze and rank the top three neighborhoods in Berlin for opening a new family-oriented café, considering scores for both 'family-friendly areas' and 'local cafés'" would prompt the AI to sequentially query the API for various Berlin districts with those category parameters, collate the numerical scores, and output a ranked list with contextual reasoning. Similarly, a request like "Generate a Python function that takes a list of hotel locations and returns the average 'tourist attraction' score for the 5km radius around each" would cause the AI to draft the complete function, including the logic to make the necessary API calls via the MCP server and process the results. This facilitates rapid automation of geospatial analysis, data enrichment pipelines, and the creation of intelligent recommendation engines within the developer's workflow.
🛡️Security & Auth
Critical to the secure and effective implementation of this integration are strict adherence to authentication and security protocols. Although the specific endpoint listed for testing may not require authentication, the production environment for this API relies on OAuth 2.0, as referenced in the Amadeus Authorization Guide. Developers must treat the API client credentials (API Key and Secret) with the highest secrecy, never hardcoding them in client-side applications or repositories. When configuring the MCP server, these credentials should be injected via secure environment variables or a secret management service. Following the principle of least privilege, the access token requested should be scoped specifically to the Location Score API and any other necessary endpoints, avoiding overly broad permissions. Developers should also implement robust error handling for rate limits and API failures, and consider caching responses where data does not change frequently to reduce unnecessary calls and improve application performance.

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