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 # local-image-mcp
 
+<p align="center">
+  <img src="assets/logo.jpg" alt="local-image-mcp logo" width="256"/>
+</p>
+
 Minimal Model Context Protocol server for local image inspection.
 
 `local-image-mcp` exposes two tools for debugging workflows:
@@ -81,6 +85,34 @@ Response content includes:
 - a text confirmation
 - an `image` item with base64 data and MIME type
 
+## Usage example
+
+The following example shows how an AI agent can use `local-image-mcp` together with a Python script to automatically detect and remove a watermark from an image, keeping the subject centred.
+
+**Prompt given to the agent:**
+
+> Crop the image in `assets/` to remove the watermark while keeping the drawing centred. Verify the output and overwrite the original.
+
+**What the agent did:**
+
+1. Called `list_local_images` on `assets/` to discover `logo.jpg`.
+2. Called `load_local_image` to inspect the file visually.
+3. Analysed pixel data to locate the watermark (a 4-pointed star in the bottom-right corner at roughly `x: 944–991, y: 944–991` on a 1024×1024 image).
+4. Detected the subject bounding box (`x: 153–870, y: 256–808`, centre ≈ 511, 532).
+5. Computed a square crop centred on the subject with the right/bottom edge stopping at `940 px` (just before the watermark):
+
+```python
+from PIL import Image
+
+img = Image.open("assets/logo.jpg")
+cx, cy = 511, 532          # subject centre
+half   = min(940 - cx, 940 - cy)   # 408 px
+box    = (cx - half, cy - half, cx + half, cy + half)
+img.crop(box).save("assets/logo.jpg", quality=95)
+```
+
+6. Called `load_local_image` again to verify the result — watermark gone, subject centred — then overwrote the original and removed the intermediate file.
+
 ## Resources
 
 Every successfully loaded image is cached for the current server session and published as a resource:
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