Wandy3D

Make a 3D model from a description

generate_from_text

Make a textured 3D model of one object from a text description, when there is no image. Paid — 10 credits for the internal reference picture plus the model price (30–60 credits, by 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_text"
  }
}

Input Schema

Name	Required	Description	Default
`prompt`	No	What to make, 3–500 characters: one object, its shape, colours and materials, e.g. "a red ceramic teapot with a bamboo handle"
`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
`concept_id`	No	Returned when the picture was made but the model did not start: pass it to retry without paying for a new picture (no prompt needed)

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 12, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Goes well beyond the annotations: discloses that the operation is paid, breaks down the cost (10 credits for the reference picture plus 30–60 by quality), requires user approval before spending, and that it is asynchronous, returning a task_id resolved via get_task in a few minutes. This is exactly the behavioral context the non-readOnly/non-idempotent hints cannot convey.

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?

Four information-dense clauses with zero filler, front-loaded with the core action and followed by cost, approval gate, and the follow-up tool. Slightly heavy parenthetical nesting keeps it from being effortless to scan.

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 paid, asynchronous generation tool with no output schema, it covers everything an agent needs: cost and approval gate, expected latency, the returned task_id, and which tool to call next.

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%, so the schema already documents prompt, quality tiers, prices and concept_id. The description only reinforces the price-by-quality relationship and adds nothing about concept_id, the retry path, or prompt constraints.

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?

States a specific verb+resource+scope: 'Make a textured 3D model of one object from a text description.' The phrase 'when there is no image' distinguishes it from the image-based sibling without needing to open schemas.

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 selection condition ('when there is no image') and a mandatory prerequisite workflow ('tell the user the price and get their OK first'). It stops short of naming the image-based alternative explicitly, so routing is implied rather than spelled out.

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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