Wandy3D

Make a 3D model from an image

generate_from_image

Turn one photo, render or drawing of an object into a textured 3D model. Paid — 30–60 credits by version (quality): tell the user the price and get their OK first. Returns a task_id; get_task shows the model when it is ready (a few minutes).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "generate_from_image"
  }
}

Input Schema

Name	Required	Description	Default
`quality`	No	Wandy version: ultra = Wandy 2 (default, best for most objects; 60 credits), quality = Wandy 1.1 (best for cars; 50 credits), fast = Wandy 1 (cheapest and fastest, less detail; 30 credits)	ultra
`image_url`	No	https:// link to the image (JPEG, PNG or WebP up to 10 MB; one object on a plain background works best). Give this or image_base64.
`image_base64`	No	The image as base64 or a data: URL. Give this or image_url.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 12, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Adds substantial context beyond annotations: the operation is paid (30–60 credits), cost varies by quality version, and the call is asynchronous, returning a task_id that completes in a few minutes. This pricing and approval requirement is exactly the kind of behavioral detail annotations cannot express.

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?

Three compact sentences, each earning its place: the transformation, the cost/approval constraint, and the async follow-up. Front-loaded with what the tool does.

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

Completeness5/5

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

For a 3-param async generation tool with no output schema, the description covers the cost, the approval step, the immediate return (task_id), and how to get the result. Nothing essential for correct invocation is missing.

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

Parameters3/5

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

Schema coverage is 100% and the quality enum already documents the version/credit mapping. The description's 'by version (quality)' mirrors that without adding new detail, so baseline 3 applies.

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

Purpose4/5

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

States a specific transformation verb and resource: 'Turn one photo, render or drawing of an object into a textured 3D model.' The input-medium emphasis implicitly separates it from generate_from_text, though no sibling is named directly.

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?

Gives a clear operational precondition ('tell the user the price and get their OK first') and routes the agent to get_task for retrieval. It doesn't explicitly say when to prefer it over generate_from_text or make_low_poly, leaving that inference to the agent.

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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