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API Best Practices

To ensure a smooth integration and reliable operation, we recommend following these best practices when building with the MetService API Platform.

1. Design for Clarity​

  • Audience-Centric Approach: Design your application to handle weather data in a way that is meaningful to your end-users. Don't just pass through raw data; interpret it (e.g., "High Wind Warning" vs. "45kt gusts").
  • Explicit Naming: When mapping our data to your internal models, preserve our "glass box" naming convention (phenomenon_condition_statistic) to avoid scientific ambiguity.

2. Robust Implementation​

  • Handle Errors Gracefully: Always implement logic to handle HTTP 429 Too Many Requests (Rate Limits) and 5xx Server Errors. Use exponential backoff for retries.
  • Respect Rate Limits: Cache responses locally where possible. Weather data does not change every second; polling a forecast endpoint every 10 seconds is unnecessary and will deplete your quota.
  • Validate Inputs: Ensure your requests use valid ISO 8601 dates and correct location identifiers before sending them to the API.

3. Testing & Validation​

  • Mocking & Simulation: Since we do not currently provide a sandbox environment, we recommend creating a suite of mock responses (saved from successful API calls) to simulate different weather conditions during your development and testing cycles.
  • Monitor Usage: Keep an eye on your response headers for rate limit usage to prevent unexpected service interruptions in production.
  • Security: Never commit your API keys to public repositories. Use environment variables (e.g., dotenv) to manage credentials securely.