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post-models-training-images-by-model-id

Add training images to a specific AI model for improving its generation capabilities. Upload images via data URL or asset ID to enhance model training.

Instructions

Add a new training image to the given modelId

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
originalAssetsNoIf set to true, returns the original asset without transformation
modelIdYesThe `modelId` where the training image will be stored
dataNoThe training image as a data URL (example: "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVQYV2NgYAAAAAMAAWgmWQ0AAAAASUVORK5CYII=")
assetIdNoThe asset ID to use as a training image (example: "asset_GTrL3mq4SXWyMxkOHRxlpw"). If provided, "data" and "name" parameters will be ignored.
nameNoThe original file name of the image (example: "my-training-image.jpg")
assetIdsNo
presetNoThe preset to use for training images
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states this is an 'Add' operation, implying creation/mutation, but doesn't disclose behavioral traits like required permissions, whether it's idempotent, rate limits, or what happens on success/failure (e.g., returns a training image ID). For a mutation tool with zero annotation coverage, this is a significant gap.

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 that front-loads the core purpose without fluff. Every word earns its place, making it easy to parse quickly.

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?

Given the tool's complexity (7 parameters, mutation operation) and lack of annotations/output schema, the description is incomplete. It doesn't cover behavioral aspects (e.g., side effects, error handling), parameter dependencies, or output expectations. For a tool that adds training images to models, more context is needed to use it effectively.

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 high (86%), so the baseline is 3. The description adds no parameter semantics beyond the schema—it mentions 'modelId' but doesn't explain parameter interactions (e.g., 'assetId' overrides 'data' and 'name') or provide usage examples. The schema already documents parameters well, so no extra value is added.

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 action ('Add a new training image') and target resource ('to the given modelId'), which is specific and unambiguous. However, it doesn't distinguish this tool from sibling tools like 'post-asset' or 'put-models-training-images-by-model-id-and-training-image-id', which appear to handle similar image-related operations for models.

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. It doesn't mention prerequisites (e.g., model must exist), exclusions (e.g., not for updating existing images), or refer to sibling tools like 'put-models-training-images-by-model-id-and-training-image-id' for updates or 'post-asset' for general asset uploads.

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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