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GoModelHub 3D MCP

GoModelHub 3D MCP

Official
by gomodelhub
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

generate_3d

Generate 3D models from text prompts or image URLs by submitting a generation job, returning a task ID for tracking.

Instructions

Submit a 3D generation job to GoModelHub. Supports text/URL (JSON) or local image file (multipart). Returns taskId.

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_3d"
  }
}

Input 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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.3.1

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that the tool is asynchronous (returns a taskId rather than the 3D result), and supports two submission mechanisms. However, it does not mention failure behavior, rate limits, authentication, or what happens on invalid input. It is adequate but not rich.

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?

Two sentences with no filler. The core purpose and return value are front-loaded, followed by input format specifics. Every word earns its place.

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

Completeness3/5

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

The description is minimal for a tool with 6 parameters and no output schema. It explains the return value (taskId) but not the full response structure or that the job is non-blocking and should be polled via get_3d_status. It also doesn't explain the difference between the two submission formats beyond what the schema already says. An agent could call it correctly, but it would need to infer the async workflow.

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 each parameter already has a description. The tool description adds context about JSON vs multipart submission but does not clarify relationships between parameters (e.g., when mode is required, or how imagePath differs from image). Baseline 3 is appropriate when the schema does the heavy lifting.

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 clear verb ('Submit'), a specific resource ('a 3D generation job to GoModelHub'), and the expected output ('Returns taskId'). It distinguishes itself from siblings by focusing on submission, while get_3d_status likely retrieves status and generate_3d_and_wait likely blocks for completion.

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

Usage Guidelines2/5

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

The description mentions supported input modes (JSON vs multipart) but provides no guidance on when to use this tool over generate_3d_and_wait. It does not explicitly state that this is fire-and-forget, that callers should poll with get_3d_status, or that generate_3d_and_wait is the blocking alternative. The only hint is 'Returns taskId', implying async, but the sibling distinction is left unstated.

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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