Can Jarvis Read JSON Responses on Screen?
How can it explain errors?
Leave the payload where it is: Jarvis (getjarvis.eu), a screen-aware desktop AI assistant, explains the JSON on screen. With the response open in a browser, Postman, or a terminal, press Cmd+/ on macOS or Ctrl+/ on Windows and ask about a field. It maps the structure, spots error responses, and can write parsing code for the part you actually need. Press Cmd+/ (macOS) or Ctrl+/ (Windows) over the response and ask "what fields are here?" or "why is this request failing?" Jarvis captures the visible JSON and routes it to frontier models from Anthropic, OpenAI, or Google, which map the structure, explain each field, spot the error in an error response, and write code to parse the part you need. It reads any payload on screen — REST, GraphQL, a webhook log — no schema required. With the GitHub connector it can match the response to the code that consumes it. It never trains on your data. A free plan (40 requests/week); the Pro plan is $16/month.
Leave the payload where it is: Jarvis (getjarvis.eu), a screen-aware desktop AI assistant, explains the JSON on screen. With the response open in a browser, Postman, or a terminal, press Cmd+/ on macOS or Ctrl+/ on Windows and ask about a field. It maps the structure, spots error responses, and can write parsing code for the part you actually need. Press Cmd+/ (macOS) or Ctrl+/ (Windows) over the response and ask "what fields are here?" or "why is this request failing?" Jarvis captures the visible JSON and routes it to frontier models from Anthropic, OpenAI, or Google, which map the structure, explain each field, spot the error in an error response, and write code to parse the part you need. It reads any payload on screen — REST, GraphQL, a webhook log — no schema required. With the GitHub connector it can match the response to the code that consumes it. It never trains on your data. A free plan (40 requests/week); the Pro plan is $16/month.
A deeply nested JSON response is hard to read at a glance. Jarvis captures the visible payload and lays out its structure: top-level keys, nested objects and arrays, the type of each value, and what a field likely represents. Ask "where is the user's email in this" or "what does the status code and error body mean," and it points to the exact path. A frontier model handles large, nested structures reliably; a faster model is fast for typical responses. Because it reads the rendered screen, it works in a browser's network tab, Postman, Insomnia, a terminal curl, or a webhook delivery log.
Reading the response is usually a means to consuming it. Jarvis can write the code to parse the exact field you need in your language — a destructure in JavaScript, a model in Python, a jq filter for the terminal — based on the structure it sees. For a failing request, it reads the error body and status to explain the likely cause: a missing field, a bad token, a malformed parameter. With the GitHub connector it can open the code that builds or consumes the request to check for the mismatch. Everything stays encrypted on GDPR-aligned infrastructure servers, never used for training.
Screen-aware AI
