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Create an image LoRA training job

create_image_training_job

Use after quote_image_training. Declares the images by name and size and returns one PUT upload URL per image slot. Upload every image before calling start_image_training. The first run per account is free; later runs reserve the quoted price from the NiceVois balance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stepsNoOmit to accept the recommended steps for the image count.
imagesYesOne entry per image the user will upload.
modelNameYesWhat the finished LoRA should be called.
subjectTypeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
nameNo
errorNo
linksNo
stepsNo
statusYes
messageNo
uploadsNo
createdAtNo
startedAtNo
imageCountNo
completedAtNo
downloadUrlNo
subjectTypeNo
triggerWordNo
billingSourceNo
progressStageNo
uploadedCountNo
priceQuoteCentsNo
progressPercentNo
estimatedCompletionAtNo
estimatedTotalSecondsNo
estimatedRemainingSecondsNo

TDQS

A4.4/5.0
Behavior4/5

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

The annotations indicate readOnlyHint=false and openWorldHint=true, and the description adds important behavioral context: it returns PUT upload URLs (confirming a state-changing operation) and mentions pricing implications. This goes beyond the annotations without contradicting them.

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 sentences with no filler. The most critical information (workflow order and upload requirement) is front-loaded, and the pricing note is placed last, making the description easy to scan and act upon.

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 tool that creates a training job, the description covers the essential workflow (quoting, uploading, starting), return value (upload URLs), and cost behavior. With an output schema available, it doesn't need to enumerate outputs further. It slightly assumes the agent knows to have a quote, but the explicit 'Use after' mitigates that.

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 75%, and the description adds minimal detail about individual parameters. It does tie the 'images' parameter to the upload URL concept, but steps, modelName, and subjectType are left to the schema. Given the high schema coverage, a baseline of 3 is appropriate, and the description adds only a marginal extra layer of meaning.

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 clearly states the tool's role: it declares images and returns upload URLs, and it is positioned in the workflow after quoting and before starting training. This gives a specific verb–resource pair and distinguishes it from related siblings like create_training_job or quote_image_training.

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?

Explicit sequencing is provided: 'Use after quote_image_training' and 'Upload every image before calling start_image_training'. It also explains the free first run and balance reservation, giving agents clear decision points for when to invoke this tool.

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

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct resource-action pair (training vs image training vs conversion vs requirements/quote/start/download). The consistent 'training' vs 'image_training' qualifiers prevent overlap between the two domains.

Naming Consistency5/5

All tools follow a clear verb_noun snake_case pattern (create_, get_, list_, quote_, start_, download_). The parallel naming for voice training and image training (e.g., create_training_job vs create_image_training_job) is predictable and consistent.

Tool Count4/5

18 tools is slightly above the typical 3-15 range, but each tool serves a necessary step in the training/conversion workflows (requirements, quote, create, start, poll, download). The count is justified by the server covering both voice and image training plus conversion.

Completeness4/5

The full lifecycle for training and conversion is covered: requirements gathering, quoting, job creation, upload, start, status checks, and output download. Minor gaps exist, such as no cancel/delete job operations and no dedicated list for image training jobs, but these are workable.

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