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flow_generate_image

Generate images from a text prompt with selectable models and aspect ratios. Prepares a confirmation screen by default; with auto-confirm, verifies mode and clicks Generate.

Instructions

⚠️ THESE IMAGES CONSUME CREDITS. By default (auto_confirm=false): fills the prompt, selects model/ratio, takes a screenshot and returns "ready_for_confirmation". Does NOT click Generate. When auto_confirm=true: first verifies the UI is in IMAGE mode (not Video), that the model is an image model, takes a verification screenshot, THEN clicks Generate and waits for the images. NANO/BANANA image models only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandNoBrand context for automatic model selection: premium, standard.
modelNoModel to use: Nano Banana Pro, Nano Banana 2, or Imagen 4.Nano Banana 2
ratioNoAspect ratio: 1:1, 16:9, 9:16, 4:3, 3:4.1:1
promptYesThe text prompt for image generation.
campaignNoCampaign identifier for project matching (e.g., "summer-2026", "new-collection").
ingredientsNoNames of existing project images/characters to reference via "@name" (e.g., ["Bob the Astronaut", "Image 3"]). Use flow_list_mention_options to discover available names.
skip_configNoSkip configuration of aspect ratio, model type, and quantity in the UI. Respect manual settings.
auto_confirmNo⚠️ CREDITS. If false (default): only prepares, consumes nothing. If true: verifies that Image mode is active, THEN clicks Generate (consumes credits).
project_nameNoName for the project (will reuse existing project with same campaign, or create new).
reference_imagesNoPaths to local reference images to upload (optional).
Behavior5/5

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

With no annotations, the description fully discloses behavior: it explains credit consumption, the default non-destructive preparation flow, and the verification steps before clicking Generate. It also notes the model restriction and screenshot behavior. This is highly transparent.

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 appropriately sized for the complexity, front-loaded with a credit warning. It uses structured phrasing to explain both modes, with no wasted words. Each sentence contributes to understanding the tool's behavior.

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

Completeness4/5

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

For a 10-parameter tool with no annotations or output schema, the description covers the essential behavior, constraints, and credit implications. It could be more specific about return values or error handling, but the core operation is well explained. The schema covers parameter details, so the description complements it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds critical semantics for auto_confirm (no credits by default, verification before generating) and clarifies the overall process. It doesn't explain every parameter, but the schema already does, so the description adds value beyond the schema for the most important behavior.

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 generates images via Flow, distinguishing between default preparation mode and auto-confirm generation mode. It mentions 'NANO/BANANA image models only,' scoping its purpose. The name and description together clearly identify it as an image generation tool, distinct from flow_generate_video.

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

Usage Guidelines4/5

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

The description implies usage for image generation, with clear context that it works only with NANO/BANANA image models. It contrasts with video mode ('not Video'), indirectly guiding to use flow_generate_video for video. However, it doesn't explicitly name alternatives or provide when-not-to-use conditions.

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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