3DLogo.io

Generate a 3D model

generate_3d_model

Starts an AI generate from a text prompt or a public image URL. Spends the user's credits (see quote_generation). Returns a run id to poll with get_generation_status.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	Yes
`prompt`	No	For text mode
`density`	No		standard
`texture`	No
`image_url`	No	For image mode: a public https image (PNG/JPEG/WebP)

Output Schema

TableJSON Schema

Name	Required	Description
`runId`	Yes
`status`	Yes
`statusUrl`	Yes	Absolute https URL the user can open
`creditsCharged`	Yes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed2 schema fields changedSep 6, 2026

changedInput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

changedOutput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

First observedSep 6, 2026

TDQS

A4.4/5.0

Behavior5/5

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

The description discloses two important behaviors not fully captured by annotations: it spends the user's credits and it returns only a run id to poll rather than the final model. This makes the asynchronous, cost-bearing nature of the operation clear.

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?

Three short sentences cover the action, the input modes, the cost implication, and the follow-up polling mechanism. There is no fluff or redundant restating of the title or schema.

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?

The description covers the essential invocation path: what to provide, that it consumes credits, and how to track the result. With an output schema present and annotations supplying the safety profile, this is mostly sufficient, but the unaddressed density and texture parameters keep it from being fully complete for correct invocation.

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?

The phrase 'from a text prompt or a public image URL' adds useful meaning to mode/prompt/image_url and signals the two input modes. However, density and texture are entirely unexplained, and the required mode parameter's enum semantics are only implied. With only 40% schema coverage, this is a meaningful gap.

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 concrete action—'Starts an AI generate'—and specifies the two input sources: a text prompt or a public image URL. It clearly differentiates this tool from siblings like get_generation_status and quote_generation by positioning it as the initiation step.

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?

It explicitly directs the agent to quote_generation for credit cost and to get_generation_status for polling the returned run id, giving a clear usage path. However, it does not explicitly state when not to use this tool or compare it with other generation-related siblings like save_generation_as_project.

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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TDQS

A3.8/5.0

Disambiguation3/5

Most tools target distinct resources, but several overlapping pairs exist: search/list_projects both find projects by title, fetch/get_project both return project details, and upload_logo_image/request_logo_image_upload are two upload paths. The descriptions help clarify boundaries, but an agent could still misselect.

Naming Consistency4/5

Tool names overwhelmingly follow a clear verb_noun snake_case pattern (create_, list_, get_, update_, delete_). Minor deviations like bare 'fetch' and 'search', plus the mixed '3d' in generate_3d_model vs '3D' in descriptions, keep it from being perfectly consistent.

Tool Count2/5

At 31 tools, this exceeds the 25+ threshold where agent tool selection becomes cognitively heavy. While the server covers a broad platform, several tools are near-redundant and could be consolidated, making the count feel inflated.

Completeness4/5

The surface covers project lifecycle, sharing/publishing, AI generation, uploads, materials, and account/plan management quite thoroughly. Minor gaps exist, such as no direct create_coin_project tool and no deletion for generation runs, but these are workable.

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