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batch_generate

Process a list of prompts in parallel batches to generate multiple images with consistent settings.

Instructions

Generate multiple images from a list of prompts efficiently.

Processes prompts in parallel batches for optimal performance. All images share the same generation settings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptsYesList of text descriptions for image generation
batch_sizeNoParallel batch size (default: from config)
image_sizeNoImage resolution for all images (default: 2K)2K
aspect_ratioNoAspect ratio for all images (default: 1:1)1:1
output_formatNoImage format for all images (default: png)png
thinking_levelNoThinking level - "minimal" or "high"minimal
preserve_originalNo
alpha_output_formatNopng
enable_image_searchNoEnable Google Image Search
response_modalitiesNoResponse types (TEXT, IMAGE)
enable_google_searchNoEnable Google Web Search grounding
reference_image_pathsNoShared reference image path(s), up to 14.
transparent_backgroundNoSet True to get ready-to-use transparent PNG/WebP cut-outs for EVERY prompt via the two-pass difference matte (each prompt costs a second edit-to-black model call). The alpha file for each image is returned as "transparent_path"; pick the alpha format with alpha_output_format ("png"/"webp").

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are present, so the description must bear full responsibility for behavioral disclosure. It mentions parallel processing and shared settings but lacks details on failure modes, rate limits, memory usage, or specific model behaviors. The transparent_background parameter includes some cost context, but the main description is insufficient.

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 contains three concise sentences that front-load the purpose and key feature (parallel processing). No extraneous information is present.

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

Completeness3/5

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

Given the tool's complexity (13 parameters, batch processing), the description provides a high-level overview but lacks details on output structure, error handling, or limitations. The presence of an output schema partially compensates, but more context about batch behavior and performance trade-offs would improve completeness.

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?

The schema already covers 85% of parameters with descriptions. The main description adds no extra semantic value beyond stating that all images share the same settings. The parameter descriptions themselves are detailed, particularly for transparent_background, so the description does not significantly enhance understanding.

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

Purpose5/5

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

The description clearly states the tool's function: generating multiple images from a list of prompts efficiently in parallel batches. It explicitly differentiates from siblings like generate_image and generate_app_icon by emphasizing batch processing and shared settings.

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

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for batch generation with shared settings but does not explicitly state when to use this tool versus alternatives or when not to use it. The sibling names provide some context, but no direct guidance is given.

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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