iLook is a privacy-first, non-medical tool for people who want a clearer way to understand the visual patterns in a photo they provide. Instead of making a vague claim about appearance, iLook explains the signals it can observe and keeps the limits of that analysis visible. The core web experience is free to use without an account or payment. It is designed for photos that the user has permission to provide. iLook does not identify people, monitor anyone, infer sensitive traits, or present an appearance observation as medical advice.
The analysis combines several useful views. It estimates face-shape context from visible proportions, reports observable facial-symmetry signals, and compares selected measurements with the mathematical Golden Ratio phi as a reference point. It also produces a subjective face score with plain-language context, so the number is treated as an aid to reflection rather than an objective measure of a person. The result is a readable explanation of what the image suggests, which parts are approximate, and which conclusions should not be drawn. Lighting, pose, expression, camera distance, image quality, and styling can all affect the result.
iLook is useful for experimenting with computer-vision interfaces, learning how image measurements can be communicated responsibly, and building prototypes that need a simple analysis step. Designers can use it to test an image-analysis flow. Developers can explore the public REST/OpenAPI documentation, the hosted MCP endpoint, and the A2A agent card when evaluating tool interoperability or agent workflows. The API documentation is available at https://www.ilook.fit/api/docs. The MCP endpoint is https://www.ilook.fit/mcp, and the A2A agent card is https://www.ilook.fit/.well-known/agent-card.json. These public interfaces make it possible to understand the request and response shape before deciding whether iLook belongs in a larger prototype.
The product is intentionally straightforward: provide a suitable image, review the explanation, and decide what is useful for your own context. The service does not create a public gallery from submitted images. Users should avoid uploading images they do not have permission to use, and teams integrating an interface should make consent and the non-medical scope clear to their own users. The output is descriptive and approximate; it is not a diagnosis, identity check, biometric search, employment screen, credit assessment, or other high-impact decision.
For makers and developers, iLook offers a focused example of how an AI-assisted visual tool can be transparent about uncertainty while still being practical. The website provides the free experience and developer documentation, while the API, MCP, and A2A routes make the integration contract easy to inspect. Visit https://www.ilook.fit/ to try the experience, read the documentation, or explore the developer interfaces. iLook aims to keep inputs clear, outputs separated, and claims narrow enough to be useful without pretending that an automated visual estimate is objective truth.
