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post-model-preset-by-model-id

Create a custom preset for a specific AI model to standardize image generation settings and ensure consistent output quality across projects.

Instructions

Create a new preset for the given modelId

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
originalAssetsNoIf set to true, returns the original asset without transformation
modelIdYesThe preset's `modelId`
isDefaultNoWhether this preset should be the default preset for the model
inferenceIdYesThe inference ID used to generate new 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 'Create a new preset', implying a write/mutation operation, but doesn't disclose behavioral traits such as required permissions, whether it's idempotent, rate limits, or what happens on success/failure (e.g., returns a preset ID). This leaves significant gaps for a mutation tool.

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 directly states the tool's purpose without fluff. It's appropriately sized and front-loaded, 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 this is a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects (e.g., side effects, error handling) or output details (e.g., what's returned after creation). The 100% schema coverage helps with inputs, but overall context is lacking for safe and effective use.

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 100%, so parameters are well-documented in the schema. The description adds no meaning beyond the schema—it doesn't explain how parameters interact (e.g., 'modelId' ties to an existing model, 'inferenceId' for image generation) or provide usage examples. Baseline 3 is appropriate as the schema does the heavy lifting.

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 'Create a new preset for the given `modelId`' clearly states the action (create) and resource (preset), but it's vague about what a 'preset' entails (e.g., configuration settings, image generation parameters) and doesn't differentiate from sibling tools like 'post-models' or 'put-model-preset-by-model-id-and-preset-id'. It avoids tautology but lacks specificity.

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. It doesn't mention prerequisites (e.g., needing an existing model), exclusions, or compare to similar tools like 'put-model-preset-by-model-id-and-preset-id' (update) or 'get-model-presets-by-model-id' (list). The description implies usage only through the verb 'create' without context.

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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