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generate_from_image

Turn your own picture into a Roblox-ready 3D asset (skips text-to-image). Pass a public https URL of a PNG, JPG or WebP (≤10 MB, at least 256 px on the short side). Best input: one object, fully in frame, on a plain background. Returns a generation_id immediately; poll get_generation until status is completed. Local files are not reachable from this server: host the image somewhere public first, or upload it with POST /api/v1/generations/from-image.

Input Schema

NameRequiredDescriptionDefault
nameNoOptional library label. Defaults to the image's file name.
variantsNoNumber of 3D variants of the same image (1–4, different seeds). Default 1. Each variant counts against quota; 2–4 need a paid plan.
image_urlYesPublic https URL of the image. Private, internal and IP-address hosts are refused.
size_studsNoOptional real-world size: the longest side of the finished model in studs (1 stud = 0.28 m). Omit to use the model's default size.
tri_budgetNoTriangle budget (500–10000). Default 10000. Models are decimated to it, then measured.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and explains the asynchronous flow: it returns a generation_id immediately and requires polling get_generation until completion. It also discloses input constraints and that local files are unreachable, though it does not cover auth, rate limits, or quota behavior in detail.

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 front-loaded with the core purpose, then moves efficiently through input requirements, best practices, async behavior, and local-file handling. Every sentence provides actionable information with no filler.

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 five-parameter generation tool with no annotations or output schema, the description is largely complete: it covers accepted input, immediate return behavior, polling, and local-file alternatives. It could say more about final output or quota implications, but the schema covers the quota detail for variants.

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 all five parameters thoroughly. The description adds useful input-quality guidance for image_url, such as accepted formats, size limits, and ideal image composition, but adds little for name, variants, size_studs, or tri_budget beyond what the schema provides.

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 specific verb and resource: turning a user's own picture into a Roblox-ready 3D asset. It explicitly distinguishes this from text-to-image generation, which lets an agent separate it from generate_asset without opening the schema.

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 clear when-to-use guidance: pass a public image URL when converting an existing picture rather than generating from text. It also names the follow-up tool get_generation for polling and gives an alternative upload endpoint for local files.

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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