npx github:simen/mcp-see

git clone https://github.com/simen/mcp-see.git
cd mcp-see
npm install
npm run build

{
  "mcpServers": {
    "mcp-see": {
      "command": "npx",
      "args": ["github:simen/mcp-see"],
      "env": {
        "GOOGLE_CLOUD_PROJECT": "your-project-id",
        "OPENAI_API_KEY": "sk-...",
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

{
  "image": "/path/to/image.png",
  "prompt": "What is shown in this image?",
  "provider": "gemini",
  "detail": "detailed"
}

The image shows a vibrant and colorful salad bowl, viewed from directly above.
The bowl is made of a light brown, possibly biodegradable material. The salad
is composed of various ingredients arranged in distinct sections: two small
white peeled eggs, sliced red tomatoes topped with chopped green onions, cubed
seasoned tofu, bright green edamame beans, shredded purple cabbage, and
julienned carrots...

{
  "image": "/path/to/image.png",
  "prompt": "find all TV screens"
}

{
  "count": 3,
  "objects": [
    { "id": 1, "label": "television", "bbox": [178, 245, 433, 818] },
    { "id": 2, "label": "television", "bbox": [614, 518, 792, 898] },
    { "id": 3, "label": "television", "bbox": [617, 198, 792, 493] }
  ]
}

{
  "image": "/path/to/image.png",
  "bbox": [200, 200, 800, 800],
  "prompt": "describe this in detail",
  "provider": "gemini"
}

{
  "bbox": [200, 200, 800, 800],
  "description": "The image showcases a vibrant and colorful salad bowl in close-up. The bowl contains fresh ingredients including cubed tofu with a seasoned exterior, bright green edamame, sliced tomatoes, and shredded purple cabbage..."
}

{
  "image": "/path/to/image.png",
  "bbox": [100, 200, 400, 600],
  "top": 5
}

{
  "dominant": [
    {
      "hex": "#e6e6e5",
      "rgb": [230, 230, 229],
      "hsl": { "h": 60, "s": 2, "l": 90 },
      "name": "Ambience White",
      "percentage": 75.91
    },
    {
      "hex": "#b16c39",
      "rgb": [177, 108, 57],
      "hsl": { "h": 26, "s": 51, "l": 46 },
      "name": "Ginger Dough",
      "percentage": 15.91
    }
  ],
  "average": {
    "hex": "#c4b8a8",
    "rgb": [196, 184, 168],
    "name": "Doeskin"
  },
  "confidence": "high",
  "region": {
    "bbox": [100, 200, 400, 600],
    "size": [200, 150],
    "totalPixels": 30000
  }
}

1. describe(image)
   → "A shelf displaying various vintage electronics and TVs"

2. detect(image, "find all screens")
   → [{label: "television", bbox: [178, 245, 433, 818]}, ...]

3. describe_region(image, [178, 245, 433, 818])
   → "A vintage CRT television with wood grain casing, displaying
      a test pattern. The screen shows horizontal color bars..."

4. analyze_colors(image, [178, 245, 433, 818])
   → dominant: ["#2b1810" Espresso Bean, "#c4a882" Sandcastle, ...]

1. describe(image, "explain this UI to a web developer")
   → Layout structure, component hierarchy, spacing patterns

2. detect(image, "find all buttons")
   → Bounding boxes for each button

3. For each button:
   - describe_region() → Button label, icon, state
   - analyze_colors() → Exact color tokens for CSS

const pixelX = (normalizedX / 1000) * imageWidth;
const pixelY = (normalizedY / 1000) * imageHeight;