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put-models-train-by-model-id

Start training an AI model using its model ID with customizable parameters for LoRA training, validation prompts, and learning rate adjustments.

Instructions

Trigger the given modelId training

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelIdYesThe training's `modelId` to trigger
originalAssetsNoIf set to true, returns the original asset without transformation
dryRunNo
parametersNo
Behavior2/5

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

With no annotations, the description carries full burden but only states 'trigger' without clarifying behavioral traits. It doesn't disclose if this is a mutating operation (likely, given 'put'), potential side effects (e.g., resource consumption, time), authentication needs, rate limits, or expected outcomes. This leaves critical behavioral aspects undocumented.

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, front-loading the core action. It's appropriately sized for a tool name that implies its function, though it could benefit from more detail given the complexity.

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 (4 parameters with nested objects, no output schema, no annotations), the description is inadequate. It doesn't explain the training process, expected inputs beyond 'modelId', or what 'trigger' entails, leaving significant gaps for a mutating operation with many configurable options.

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 50%, and the description mentions only 'modelId' without adding meaning beyond the schema. It doesn't explain the purpose of 'originalAssets', 'dryRun', or the complex 'parameters' object, failing to compensate for the low coverage. However, the schema provides detailed param descriptions, so baseline 3 applies.

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

Purpose3/5

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

The description 'Trigger the given `modelId` training' states a verb ('trigger') and resource ('training'), but is vague about what 'trigger' means (e.g., start, resume, schedule) and lacks specificity about the training process. It doesn't distinguish from siblings like 'post-model-training-action-by-model-id' or 'put-models-by-model-id', leaving ambiguity about its unique role.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing a configured model), exclusions, or comparisons to sibling tools such as 'post-model-training-action-by-model-id', leaving the agent with no contextual usage cues.

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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