Thicken MCP server

Photos in. Out comes the object: its 3D model and its metadata.

thicken turns photos of a real object into a GLB model at its real size and its metadata as JSON, read off the object by vision models and looked up in public catalogs. What it knows about each kind of object lives in a profile: a few come with thicken, and you can add your own.

In each, the photos going in and the model coming out, with its metadata, like the one below. The logo above is a thicken model too, made by its lettering profile: thicken lettering thicken.

The other half: its metadata. Part of the first one's, a graded Giannis Antetokounmpo card:

{
  "profile": "slab",
  "holder": "cgc",
  "grader": "cgc",
  "cert": "6072896005",
  "widthMm": 80.5, "heightMm": 135, "depthMm": 7.5,
  "read": {
    "grade": "9", "gradeName": "MINT",
    "autoGrade": "10", "autoGradeName": "AUTO",
    "signed": "G. ANTETOKOUNMPO, 8/2/25",
    "name": "Giannis Antetokounmpo", "year": "2024-25", "maker": "Panini",
    "set": "Court Kings Basketball", "number": "#5", "variant": "Blank Slate"
  },
  "readBy": { "grade": "agreed", "autoGrade": "agreed", "set": "disputed" },
  "inside": {
    "card": { "widthMm": 63.5, "heightMm": 88.9, "corners": "square", "foil": "none" },
    "label": { "widthMm": 66.4, "heightMm": 26.9 }
  }
}

The grader and certificate number come from the label's QR code; the vision models read the rest off the slab. What each profile records is in the table of profiles, and every field is described in USAGE.md.

Shortened. The full README is on GitHub.