Automatically transform GPS coordinates into a rich, 28-field data payload by querying free APIs in parallel.
This AI agent converts GPS coordinates into a comprehensive 28-field data payload in a single response. It aggregates address, timezone, weather, and sun data from free services and formats it for easy integration. It enables location-based apps, travel platforms, and IoT projects to deliver richer context without vendor lock-in.
Core actions the agent performs to deliver the data.
Receive coordinates via webhook and validate inputs
Query OpenStreetMap, TimezoneDB, Sunrise-Sunset, and OpenWeatherMap in parallel
Merge responses into a unified 28-field structure
Format data with consistent units and formats
Return a single JSON endpoint response
Log errors and gracefully handle rate limits and retries
Concrete workflow implications of adopting this AI agent.
A simple 3-step flow to go from coordinates to enriched data.
The AI agent accepts lat and lon via a webhook GET request and validates inputs.
The AI agent triggers parallel requests to OpenStreetMap, TimezoneDB, Sunrise-Sunset, and OpenWeatherMap and collects responses.
The AI agent merges, formats, and returns a 28-field JSON payload.
One realistic scenario.
Scenario: A mobile app requests location details for a user at coordinates 27.1751, 78.0396. The AI agent returns a 28-field JSON payload including address, timezone, weather, and sun data within about 1-3 seconds.
One supporting sentence.
Need enriched location context to power maps and geofenced features.
Provide precise local information for itineraries and recommendations.
Trigger location-based automations with reliable data.
Align routing with live weather and sun data for safety and efficiency.
Access uniform, rich location data for analytics.
Experiment with integrated geodata in projects without costs.
One supporting sentence with short explanation.
Fetch live weather data (temperature, humidity, pressure, conditions) for the coordinates.
Retrieve time zone name, abbreviation, and current local time for the coordinates.
Resolve address components (suburb, city, state, country) using coordinates.
Provide sunrise and sunset times and day length for the location.
One supporting sentence with short explanation.
One supporting sentence with short explanation.
The 28 fields cover address components (suburb, city, state, country, postcode), timezone name and offset, current local time, live weather (temperature, humidity, pressure, conditions with icon), sun times (sunrise, sunset, day length), and visual assets (weather icons and country flag URLs). The data is normalized for consistent use across apps. Each field is designed to be easy to map in UI dashboards and maps. The payload is returned as a single JSON object for simple integration. If a field is unavailable from a source, the agent will populate it with nulls or defaults to maintain structure.
Most of the included free services in this workflow require keys (e.g., OpenWeatherMap, TimezoneDB). The setup steps describe how to obtain and configure keys for these services. OpenStreetMap and Sunrise-Sunset typically do not require keys. You should monitor quota usage and implement caching to stay within free tier limits. The agent handles key management within the integration nodes to keep your workflow tidy.
The system runs API calls in parallel, which minimizes total latency to typically 1–3 seconds. Caching can dramatically reduce repeat requests for the same coordinates, especially in high-traffic apps. The design uses robust error handling to return meaningful responses even when a source is temporarily unavailable. You can adjust cache duration and fallback behavior based on your needs.
The current design targets single-coordinate lookups via a webhook. For bulk usage, you can extend the workflow to queue multiple requests or run on a batch endpoint. Parallel calls still help, but you’ll need an orchestration layer to manage concurrency and result aggregation. Proper rate limit awareness is recommended to avoid throttling.
The agent processes coordinates that are provided by your client and returns enriched data to your endpoint. No data is published externally by the agent itself. Ensure your implementation follows your privacy policy and regional data handling rules. If needed, you can add an additional layer to redact or tokenize sensitive inputs before processing.
Yes. The endpoint is designed to be extended with additional data sources or fields. You can substitute sources with equivalents or add new ones while preserving the 28-field payload structure. Customization primarily involves adjusting the data-merging logic and the mapping to output fields. Documented hooks exist to modify behavior without breaking the existing API contract.
The workflow already uses parallel requests, which increases throughput but also makes respecting rate limits important. Implement client-side throttling and server-side backoff strategies where necessary. The system can be configured to stagger requests or fall back to cached data during peak times to maintain reliability.
Automatically transform GPS coordinates into a rich, 28-field data payload by querying free APIs in parallel.
