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bikemeardsley

thicken

by bikemeardsley
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TypeScript
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Make a 3D model with thicken

thicken_make

Turn photos or ISBN of one real object into a real-size GLB model and matching JSON metadata, with file paths and key facts returned.

Instructions

Turns photos of one real object (or artwork, or words, or a book's ISBN) into two files that belong together: a GLB model at the object's real size, in millimeters, and its metadata as JSON, read off it by vision models and looked up in public catalogs (thicken_profiles lists each kind of object and the metadata it records). Writes both files and returns their paths and the main facts. Photos need not be cropped when a vision roster is set up (THICKEN_VISION_URL, THICKEN_VISION_MODELS); without one, give straight-on photos cropped to the object, and name the profile. One object a call: for several, call once each.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ptNoA thick trading card's thickness in points (thousandths of an inch: 55, 75, 100, 130, 180)
outNoWhere to write the GLB (an absolute path ending in .glb); the sidecar goes beside it as .json, never over a .json that isn't a thicken sidecar. Default: beside the first picture, named after it
isbnNoA book's ISBN, 10 or 13 digits: its size and catalog record are looked up
holderNoA graded slab's holder: psa, psa-thick, psa-tall, bgs, cgc, cgc-tall, sgc or tag
inputsNoAbsolute paths of the photos (JPEG, PNG, HEIC, WebP, AVIF, GIF, TIFF, SVG), scanner PDFs or videos (MOV, MP4) of one object, in any order and named anything. For the lettering profile, the words instead. Empty with an isbn: a book from its ISBN alone
previewNoAlso draw <name>.preview.png, both sides of the model (needs Chrome or Edge installed)
profileNoThe kind of object, if known; else thicken works it out from a code on it (an ISBN barcode, a grader's QR code) or the vision models. thicken_profiles lists them
size_mmNoThe object's real size in millimeters, any part of it, where measured: it outranks every lookup and estimate
depth_mmNoA badge's or lettering's thickness in millimeters
width_mmNoA badge's or lettering's width in millimeters

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral burden well: it says both files are written, paths and main facts are returned, and the vision setup affects input requirements. It does not cover overwrite behavior, permissions, or failure modes, but it discloses the core write/return behavior clearly.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but front-loaded with the core transformation, then appends constraints and batching rules. It is appropriately sized for a 10-parameter file-producing tool, though the parenthetical alternatives and environment variable names make it slightly heavy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with no output schema and no annotations, it covers the main context: what is produced, where paths come from, input preparation, profile naming, and batching. It is somewhat vague about the returned 'main facts' and does not discuss overwrite or error behavior, so it stops short of complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds useful operational semantics beyond the schema by explaining the vision-roster/cropping condition and telling the agent to name the profile when no roster is available, which clarifies how inputs and profile interact.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a very specific transformation: photos or an ISBN become a real-size GLB plus a JSON sidecar. It also names the sibling thicken_profiles as the place that lists profiles, so an agent can distinguish this maker tool from the profile-listing tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives concrete when-to-use conditions: with a vision roster, photos need not be cropped; without one, provide straight-on cropped photos and name the profile. It also states the batching rule, one object per call, but does not explicitly route the agent to thicken_profiles instead of this tool for profile discovery.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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