Compare HTTP Toolkit vs. Spark Inspector

You can easily scan through traffic by highlighting content type, status, and source. Or, you can use powerful filtering tools that match your messages to the most important ones. With MDN inline explanations and docs, you can examine the URL, status, headers, and body of each request, or response. You can explore message bodies using highlighting and auto formatting for JSON HTML, JS, JS and other formats, all with the help of Monaco, the editor in Visual Studio Code. Match requests precisely, jump to them as they appear, and edit any information: the target URL or method, headers, body, or method. You can either respond manually to the requests as they arrive or pass them upstream. On the way back, pause and edit the actual response. Step through HTTP traffic request-by-request, or manually mock errors and endpoints. To quickly prototype against new services or endpoints, create rules that match requests.

Spark offers a three-dimensional perspective of your application's interface along with the capability to adjust view settings dynamically during runtime, enabling you to design exceptional applications. If your app relies on notifications, Spark's notification monitor tracks each NSNotification as it is dispatched, providing a comprehensive stack trace, a detailed list of recipients, the methods invoked, and additional relevant information. This feature allows for a quick understanding of your app's architecture while enhancing debugging efficiency. By connecting your application to the Spark Inspector, you place your app's interface in the spotlight, with real-time updates reflecting your interactions. We keep track of every alteration within your app's view hierarchy, ensuring you remain informed about ongoing changes. The visual representation of your app in Spark is not only aesthetically pleasing but also fully customizable. You have the ability to alter nearly every aspect of your views, from class-level properties to CALayer transformations, and upon making any changes, Spark triggers a method within your app to directly implement that adjustment. This seamless integration fosters a more intuitive development experience, allowing for rapid iteration and refinement.