Amazon Location Service MCP Server

What is Amazon Location Service MCP Server?

Amazon Location Service MCP Server is an implementation of the Model Context Protocol (MCP) that connects LLM-powered tools and assistants to Amazon Location Service APIs. Through this server, AI agents can search for places, perform address geocoding and reverse geocoding, calculate routes, and leverage other AWS geospatial functionalities using MCP-standardized endpoints. It provides an extensible bridge between LLM environments and AWS's robust location platform, facilitating integration in both conversational and automated tool-based scenarios.

How to Configure Amazon Location Service MCP Server

1. Prerequisites

   • Ensure you have uv installed from Astral.
   • Install Python (e.g., uv python install 3.10).
   • Configure your AWS credentials (access keys or AWS profile) with permissions for Amazon Location Service.

2. MCP Server Configuration Example

   • Add the Amazon Location Service MCP server entry to your MCP client configuration file (e.g., ~/.aws/amazonq/mcp.json, .cursor/mcp.json, etc.):

     {
       "mcpServers": {
         "awslabs.aws-location-mcp-server": {
           "command": "uvx",
           "args": [
             "awslabs.aws-location-mcp-server@latest"
           ],
           "env": {
             "AWS_PROFILE": "your-aws-profile",
             "AWS_REGION": "us-east-1",
             "FASTMCP_LOG_LEVEL": "ERROR"
           }
         }
       }
     }
     

3. Environment Variables

   • Set AWS_PROFILE and AWS_REGION as needed.
   • For containerized deployments, pass credentials via --env or --env-file as appropriate.

4. Activation

   • Ensure the MCP server is enabled (not marked as "disabled": true in your configuration).

5. Optional

   • You can customize log levels or use role-based authentication according to your organization's security best practices.

How to Use Amazon Location Service MCP Server

1. Discover Available Tools

   • Use the MCP client interface or API to list tools through the tools/list endpoint. This reveals location-based functionalities such as place search and geocoding.

2. Invoke Location Tools

   • Call tools via the tools/call endpoint with relevant parameters. For example, perform place search by supplying a query and optional filters; get route directions by providing source and destination coordinates.

3. Typical Steps in a Workflow

   • In an IDE or chat application integrated with an LLM, prompt the assistant to leverage the Amazon Location Service MCP tools for tasks such as:
     • "Search for coffee shops near my office."
     • "Convert this address to latitude/longitude."
     • "What's the best route from here to the airport?"

4. Tool Approval in Clients

   • Approve tool calls if prompted (optional auto-approval is available in many clients for streamlined workflow).

5. Review Results

   • The AI assistant or automation pipeline will receive structured responses (e.g., place listings, coordinate results, route details) for further processing, display, code generation, or business logic execution.

6. Sample Command-Line Usage

   • Manually run the MCP server for testing:

     timeout 15s uv tool run awslabs.aws-location-mcp-server --region us-east-1 2>&1 || echo "Finished or timed out"
     

Key Features

• Seamless Place Search: Query business names, landmarks, and addresses across supported AWS data providers.
• Geocoding & Reverse Geocoding: Convert between addresses and latitude/longitude coordinates.
• Route Optimization: Calculate best routes, estimated travel time, and distances across map data.
• MCP Tool Discovery: Expose standardized, documented functions directly available for LLM agents.
• Secure AWS Integration: Leverage IAM roles and policies for controlled access to location queries and services.
• Low-Latency Local or Cloud Deployment: Run MCP servers locally for privacy, or remotely for always-available, scalable usage.
• Extensible & Configurable: Adapt endpoints and services as your application or organization requirements grow.

Use Cases

• Conversational Assistants: Enable LLM-powered chatbots to answer user location questions, perform live place lookups, or suggest businesses and points of interest.
• Field Service Automation: Auto-generate directions, optimize field routes and dispatch through geospatial APIs, with results surfaced in AI-driven dashboards or workflows.
• Customer Experience Agents: Help users find store locations, verify addresses, or check business hours via natural language.
• Application Development: Integrate geocoding, route planning, and proximity search as part of backend logic for web/mobile apps.
• Data Annotation: Geolocate unstructured addresses or annotate datasets with precise coordinates for analytics and visualization.

Frequently Asked Questions

Q1: What AWS permissions are required to use the Amazon Location Service MCP Server?
A1: The AWS credentials or profile used must be authorized to access Amazon Location Service actions such as geo:SearchPlaceIndexForText, geo:CalculateRoute, and any other location API actions you intend to expose.

Q2: Can I restrict responses by geography or data provider?
A2: Yes, the MCP server supports parameters for specifying regions, bounding boxes, data providers, or custom filters as per the Amazon Location Service API, which can be configured in your tool calls.

Q3: Does this server store or log any location data?
A3: By default, the server does not persist or log sensitive location queries or responses. Logging levels can be adjusted for debugging, but it's recommended to review settings for privacy compliance.

Q4: Can I run the Amazon Location Service MCP Server offline?
A4: While the server can run locally, it must connect to AWS to provide live location data. Some limited caching may be implemented, but most functions require network access.

Q5: How do I use these location tools in my LLM assistant?
A5: Once configured, simply prompt your AI assistant to use the desired location tool, or let the LLM automatically select relevant tools when handling geospatial queries. Approval and results are managed via your client interface.