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post-prompt-inferences

Generate, complete, or invent new prompts for AI image generation using various modes like completion, contextual, inventive, or structured approaches.

Instructions

Generate, complete or invent new prompts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dryRunNo
modeYesThe mode used to generate new prompt(s).
ensureIPClearedNoWhether we try to ensure IP removal for new prompt generation.
imageNoThe input image as a data URL (example: "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVQYV2NgYAAAAAMAAWgmWQ0AAAAASUVORK5CYII=") or the asset ID (example: "asset_GTrL3mq4SXWyMxkOHRxlpw") Required when `mode` is `image-editing-prompt`.
imagesNo
seedNoIf specified, the API will make a best effort to produce the same results, such that repeated requests with the same `seed` and parameters should return the same outputs. Must be used along with the same parameters including prompt, model's state, etc..
modelIdNoThe modelId used to condition the generation. When provided, the generation will take into account model's training images, examples. Only supports 'gemini-2.0-flash', 'gemini-2.5-flash', 'gpt-image-1', 'flux-kontext' and 'runway-gen4-image' for now when `mode` is `image-editing-prompt`.
temperatureNoThe sampling temperature to use. Higher values like `0.8` will make the output more random, while lower values like `0.2` will make it more focused and deterministic. We generally recommend altering this or `topP` but not both.
assetIdsNo
numResultsNoThe number of results to return.
promptNoThe initial prompt spark feed to `completion`, `inventive` or `structured` modes.
topPNoAn alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So `0.1` means only the tokens comprising the top `10%` probability mass are considered. We generally recommend altering this or `temperature` but not both.
Behavior1/5

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

No annotations are provided, so the description carries full burden. It mentions 'Generate, complete or invent' but doesn't disclose any behavioral traits: no information about whether this is a read or write operation, what permissions are needed, rate limits, output format, or side effects. For a tool with 12 parameters and no output schema, this is critically inadequate.

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 extremely concise at just one sentence with no wasted words. It's front-loaded with the core purpose, though this brevity comes at the cost of completeness. Every word earns its place, making it structurally efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (12 parameters, no annotations, no output schema), the description is severely incomplete. It doesn't explain what the tool actually does beyond vague verbs, doesn't provide usage context, and offers no behavioral details. For a tool that likely generates AI prompts with multiple modes and parameters, this is inadequate.

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%, so the schema documents most parameters well. The description adds no parameter-specific information beyond the schema. However, with high schema coverage, the baseline is 3 as the schema does the heavy lifting, though the description doesn't compensate for the remaining 25% gap (e.g., 'dryRun' is undocumented in both).

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

Purpose2/5

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

The description 'Generate, complete or invent new prompts' restates the tool name 'post-prompt-inferences' in slightly different words, making it tautological. It doesn't specify what kind of prompts (e.g., for AI models, creative writing, etc.) or what resource is being acted upon, and it doesn't distinguish this from sibling tools like 'post-prompt-editing-inferences' or 'post-caption-inferences'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

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 many sibling tools for different inference types (e.g., 'post-txt2img-inferences', 'post-img2img-inferences', 'post-prompt-editing-inferences'), there's no indication of when this tool is appropriate or what distinguishes it from other prompt-related operations.

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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