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GoModelHub 3D MCP

Official
by gomodelhub

generate_3d_and_wait

Submit a text or image prompt to generate a 3D model, then poll until the job succeeds, fails, or times out.

Instructions

Submit a 3D job (JSON or local image) and poll until succeeded/failed or timeout.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoOptional mode: text or image.
imageNoPublic https image URL for image-to-3D (JSON submit).
modelNo3D modelCode from marketplace (modelType=3d), e.g. hyper3d, neural4d, v3.1-20260211. Do NOT add tp- prefix.
promptNoText prompt. Required unless image or imagePath is set.
optionsNoVendor options. JSON submit: options object; local file: sent as metadata JSON.
imagePathNoLocal image file path for image-to-3D (stdio MCP only). Prefer image URL on Remote MCP. Max 50MB, jpg/png/webp.
timeoutSecNoMax wait seconds (default 300)
pollIntervalSecNoPoll interval seconds (default 3)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.3.1

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does reveal key behavior: it submits the job, polls, and stops only on success, failure, or timeout. It does not detail return format or side effects, but the core blocking lifecycle is clearly disclosed.

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?

One sentence, no filler, verb-first structure with a clear resource and outcome. Every word earns its place.

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

Completeness2/5

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

The tool has 8 parameters, no output schema, and no annotations, yet the description does not state what is returned on success or failure, nor does it reference sibling tools for non-blocking alternatives. An agent must infer important invocation details from names and schema.

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 description coverage is 100%, so the baseline is 3. The description adds only a generic 'JSON or local image' framing; it does not enrich parameter meaning beyond the already complete schema.

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 names a specific action ('Submit a 3D job') and a distinct completion behavior ('poll until succeeded/failed or timeout'). This clearly separates it from sibling tools generate_3d (submit only) and get_3d_status (status-only polling).

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

Usage Guidelines3/5

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

The blocking-and-waiting behavior implies this is for callers that need a finished result, but the description does not explicitly say when to choose this over generate_3d or get_3d_status. No exclusions or alternatives are named.

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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