I Spy AI – Detect AI-Generated Images, with an MCP Server

Name: I Spy AI – Detect AI-Generated Images, with an MCP Server
Availability: InStock
Author: shawhunterm

by shawhunterm·Apr 20, 2026·2 points·0 comments

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AI Analysis

●●SolidSlickSolve My Problem

OpenCV-based detection without ML models, but accuracy unproven against Hive or Illuminarty.

Strengths

•MCP server integration lets Claude and Cursor verify images before trusting them.
•Images analyzed and discarded immediately—no storage or training data collection.
•One-line MCP config install for Claude Desktop without SDK dependencies.

Weaknesses

•Classical CV approach less proven than ML-based detectors from established competitors.
•AI image detection is crowded—Hive, Illuminarty, and Sightengine already serve this market.

Post Description

I built I Spy AI to detect AI-generated images — upload one on the dashboard or call it from any MCP client (Claude Desktop, Cursor, etc.) via the analyze_image tool.

The detection runs on a separate FastAPI backend using pure OpenCV — no ML model, no GPU, just classical computer vision techniques looking for the artifacts diffusion models leave behind. The Next.js app handles auth, billing (Dodo Payments), and proxies images as raw binary to keep latency low. Async polling on the frontend.

Inspiration came from r/isthisai — people there spend a lot of energy arguing whether images are real, and existing detectors are either gated behind enterprise sales or unreliable black-box classifiers. Wanted something a normal person (or an LLM agent) could just hit, and not be data mined. Also purposefully kept an AI model out of the detection due to experimenting with Gemini and noticed it could not even accurately tell me an image that I just had it generate was AI generated.

Background: I was laid off earlier this year and have been building this full-time since Feb 5. Would love feedback on the detection accuracy, or the MCP integration.