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Generate a 3D model from text or an image

asset_generate_3d

Start a text/image → 3D generation of ONE freestanding object per task — build ground planes and assemblies from scene operations, and reuse a generated model by duplicating it with scene ops. provider=hyper3d (Rodin) takes prompt only; provider=hunyuan3d and provider=tripo3d take prompt OR imageUrl. Each provider needs its integrations_set token. Poll asset_generate_status, then import with asset_library_import source= slug=.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptNoDescription of the object to generate; describe one freestanding asset.
imageUrlNoOptional reference image URL supported by the chosen provider.
providerNoConfigured 3D-generation provider to use.hyper3d

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
providerYes
taskUuidYes
subscriptionKeyYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already cover readonly/destructive/idempotent safety. The description adds useful behavioral context beyond annotations: it is an asynchronous generation workflow requiring polling, provider-specific input constraints, and a required integrations_set token. It stops short of discussing limits, failures, or latency, but the additions are meaningful.

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 packs a high density of useful information into three focused sentences. The primary purpose is front-loaded, and provider constraints and follow-up steps are introduced in a logical order without filler or repetition.

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?

Given the rich schema coverage, output schema, and annotations, the description provides everything an agent needs to invoke the tool correctly: the object scope, the provider differences, the auth prerequisite, and the downstream polling/import steps. The explicit source and slug format for the follow-up import closes the loop.

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 schema already documents all three parameters. The description goes beyond the schema by clarifying which providers accept imageUrl versus prompt-only, and by tying the provider choice to the required integrations_set token and the eventual import source.

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 action ('Start a text/image → 3D generation') with a clear scope ('ONE freestanding object per task'). It differentiates itself from scene operations and from the later status/import steps, so an agent can distinguish it from sibling tools like asset_generate_status and asset_library_import.

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?

The description gives explicit workflow guidance: generate, then poll asset_generate_status, then import with asset_library_import using source and slug. It also tells the agent not to build ground planes or assemblies with this tool and instead use scene operations, and names the required integration token setup via integrations_set.

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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