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UModeler

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generate_3d_from_image

Generate a 3D model (GLB) from one image, or from 2–4 views of the same subject (multi-view → higher-fidelity geometry), async.

Instructions

Generate a 3D model (GLB) from one image, or from 2–4 views of the same subject (multi-view → higher-fidelity geometry), async. Single: image_url (any public http/https image, or a prior generation's files.image) OR image_path (a local file, uploaded directly — no hosting needed). Multi-view: image_urls OR image_paths, ordered [front, left, back, right] (2–4 views). Not every engine takes multi-view — check supportsMultiView from list_models(category='image-to-3d'); unsupported engines return 400. Local files win over URLs. Then wait_for_asset and read files.model. Costs credits — see list_models(category='image-to-3d') (NOT category='3d', which is the text-to-3D catalog and omits image-only engines).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
engineNoengine name from list_models(category='image-to-3d')
textureNo
image_urlNosingle hosted image URL
polycountNo
image_pathNoabsolute path to a single local image file (≤20MB)
image_urlsNomulti-view: 2–4 hosted image URLs, ordered [front, left, back, right]
ultra_modeNohigher-fidelity geometry. Only engines whose list_models entry has supportsUltraMode (currently meshy-7), and SINGLE image only — the vendor scopes it to one input image. Adds that entry's ultraCost credits. Combining it with another engine or with multi-view returns 400.
image_pathsNomulti-view: 2–4 local image file paths (each ≤20MB), ordered [front, left, back, right]

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.1.6
    • addedInput schema / properties / engine / description
      Added value: +"engine name from list_models(category='image-to-3d')"
    • addedInput schema / properties / ultra_mode
      Added value: +{
+  "description": "higher-fidelity geometry. Only engines whose list_models entry has supportsUltraMode (currently meshy-7), and SINGLE image only — the vendor scopes it to one input image. Adds that entry's ultraCost credits. Combining it with another engine or with multi-view returns 400.",
+  "type": "boolean"
+}
  2. Changed4 schema fields changedv0.1.5
    • changedInput schema / properties / image_path / description
      Previous value: -"absolute path to a local image file (≤20MB)"New value: +"absolute path to a single local image file (≤20MB)"
    • addedInput schema / properties / image_paths
      Added value: +{
+  "description": "multi-view: 2–4 local image file paths (each ≤20MB), ordered [front, left, back, right]",
+  "items": {
+    "type": "string"
+  },
+  "maxItems": 4,
+  "minItems": 2,
+  "type": "array"
+}
    • addedInput schema / properties / image_url / description
      Added value: +"single hosted image URL"
    • addedInput schema / properties / image_urls
      Added value: +{
+  "description": "multi-view: 2–4 hosted image URLs, ordered [front, left, back, right]",
+  "items": {
+    "format": "uri",
+    "type": "string"
+  },
+  "maxItems": 4,
+  "minItems": 2,
+  "type": "array"
+}
  3. First observedv0.1.0

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so thoroughly: it discloses async behavior, credit costs, local-file precedence ('Local files win over URLs'), unsupported-engine 400 errors, and the GLB/files.model result to read.

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

Conciseness5/5

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

The description is dense but front-loads the core purpose and every sentence earns its place, including the catalog disambiguation and cost warning. It is appropriately sized for an 8-parameter tool.

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?

The description covers input modes, engine compatibility, cost, error behavior, and follow-up asset access, which is substantial for a tool with no output schema. It does not explicitly state that an engine is required, but from schema and the engine param description an agent can infer it.

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 75% and the description compensates by explaining the single vs multi-view alternatives, the [front, left, back, right] ordering, accepted URL forms including prior generation files.image, and the no-hosting-needed local file option. It does not add meaning for texture or polycount, but those are self-evident or covered elsewhere.

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 opens with a precise verb and resource: 'Generate a 3D model (GLB) from one image, or from 2–4 views of the same subject (multi-view → higher-fidelity geometry), async.' It clearly distinguishes from siblings by noting the image-to-3D category and contrasting with the text-to-3D catalog in the cost note.

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

Usage Guidelines5/5

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

It gives explicit when-to-use guidance: check supportsMultiView before multi-view input, expect a 400 on unsupported engines, and use list_models(category='image-to-3d') rather than category='3d' (text-to-3D). It also prescribes the follow-up workflow: 'Then wait_for_asset and read files.model.'

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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