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post-models

Create new AI image generation models by configuring concepts, base models, and training parameters for custom generative workflows.

Instructions

Create a new model

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
originalAssetsNoIf set to true, returns the original asset without transformation
conceptsNo
collectionIdsNo
classSlugNoThe slug of the class you want to use (ex: "characters-npcs-mobs-characters"). Set to null to unset the class
nameNoThe model's name (ex: "Cinematic Realism"). If not set, the model's name will be automatically generated when starting training based on training data.
shortDescriptionNoThe model's short description (ex: "This model generates highly detailed cinematic scenes."). If not set, the model's short description will be automatically generated when starting training based on training data.
baseModelIdNoThe ID of the base model to use as a starting point for the training (example: "flux.1-dev") Value is automatically set based on the model's type. In case of doubt leave it empty.
typeNoThe model's type (ex: "flux.1-lora"). The type can only be changed if the model has the "new" status.
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states 'Create a new model', implying a write operation, but lacks details on permissions, side effects, rate limits, or response format. This is inadequate for a mutation tool with complex parameters.

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 is a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, making it easy to parse quickly without unnecessary elaboration.

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?

For a mutation tool with 8 parameters, no annotations, and no output schema, the description is incomplete. It fails to address behavioral aspects like what happens post-creation, error conditions, or how it integrates with other tools, leaving significant gaps for an AI agent.

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 75%, providing good parameter documentation. The description adds no additional meaning beyond the schema, such as explaining relationships between parameters like 'concepts' and 'baseModelId'. Baseline 3 is appropriate as the schema does most of the work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Create') and resource ('a new model'), making the tool's purpose evident. However, it doesn't differentiate from sibling tools like 'post-models-copy-by-model-id' or 'post-models-transfer-by-model-id', which also involve model creation or manipulation, so it misses full sibling distinction.

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 provides no guidance on when to use this tool versus alternatives. With siblings like 'post-models-copy-by-model-id' for copying models, there's no indication of prerequisites, context, or exclusions for creating a new model from scratch.

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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