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by syh52

Generate Image Variations

seedream_variations

Generate 2 to 15 coherent image variations from a prompt or base image for A/B testing, storyboards, product variations, and social media content.

Instructions

Generate multiple variations of an image concept using SeeDream 4.5.

⚠️ CLAUDE.AI USERS: This tool WILL TIMEOUT on Claude.ai (takes 30-90 seconds). Use "seedream_submit" instead for Claude.ai compatibility.

Use this tool to create 2-15 coherent variations based on a prompt. Great for:

  • A/B testing: Generate multiple options to compare

  • Storyboards: Create a series of related images

  • Product variations: Same product in different colors/styles

  • Exploration: See different interpretations of your concept

  • Social media: Multiple posts from one concept

Args:

  • prompt (string, required): Base concept for generating variations. Include batch keywords like "a series", "a set", "Generate X images" for best results.

  • count (number): Number of variations to generate (2-15, default: 4)

  • base_image (string, optional): Reference image to create variations from

  • size (string): Output size (default: '2K')

  • watermark (boolean): Add watermark (default: false)

  • download (boolean): Save locally (default: true)

  • download_dir (string): Local save directory

Returns: Array of variation image URLs, local paths if downloaded, and performance timing.

Prompt Best Practices (per official docs):

  • Include batch keywords: "a series of", "a set of", "Generate X images"

  • Describe variations explicitly: "4 seasonal versions", "different color schemes"

  • For storyboards: describe each panel's content in sequence

  • Keep prompts under 600 English words for best results

Examples:

  • "Generate a series of 4 seasonal variations of a cozy coffee shop interior"

  • "Create a set of 6 color variations of this minimalist logo design"

  • "Generate 4 different poses of this anime character in action"

  • "Create a 4-panel storyboard: morning coffee, commute, work, evening relaxation"

Tips:

  • The model maintains consistency across variations automatically

  • Use count=4 for quick comparisons, higher for more options

  • Adding a base_image helps maintain visual consistency

  • Note: base_image cannot be combined with batch generation (API limitation)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoOutput image size2K
countNoNumber of variations to generate (2-15)
promptYesBase prompt for generating variations. The model will create coherent variations based on this.
downloadNoWhether to download generated images to local directory
watermarkNoWhether to add 'AI generated' watermark
base_imageNoOptional base image to create variations from
download_dirNoLocal directory to save images (defaults to ./generated_images)
response_formatNoOutput format: 'markdown' for human-readable or 'json' for structured datamarkdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
usageNoAPI usage metrics
imagesYesArray of generated images
promptYesThe prompt used for generation
timingNoPerformance timing metrics
successYesWhether the generation was successful
base_imageNoBase image used for variations
generated_countYesNumber of variations actually generated
requested_countYesNumber of variations requested
Install Server

TDQS

A4.9/5.0
Behavior5/5

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

The description adds critical behavioral context beyond the sparse annotations: a 30-90 second timeout, the API limitation that 'base_image cannot be combined with batch generation,' and side effects like downloading locally, adding watermarks, and returning performance timing. These are exactly the behavioral traits an agent needs to predict runtime and side effects.

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 long but meticulously structured: a clear one-line purpose, a warning callout, bulleted use cases, an Args section with best practices, examples, and tips. The critical Claude.ai warning is front-loaded, and every section contributes actionable information, making the length justified.

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

Completeness5/5

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

The description covers use cases, return values, prompt crafting, examples, API limitations, and platform compatibility. The input schema and output schema handle the remaining structured details; nothing an agent needs to select or invoke this tool correctly is missing.

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, but the description goes further with prompt best practices (batch keywords like 'a series of', explicit variation descriptions, under 600 words), count guidance (use 4 for quick comparisons), and a base_image caveat. This adds real value beyond the schema's one-line descriptions, though not every parameter gets the same depth.

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 opens with 'Generate multiple variations of an image concept using SeeDream 4.5', a specific verb and resource that exactly matches the tool name and title. It clearly differentiates from siblings by emphasizing 'described variations' and 'coherent variations,' leaving no ambiguity about what this tool does versus seedream_generate, seedream_edit, or seedream_blend.

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

Usage Guidelines5/5

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

It explicitly instructs 'Use this tool to create 2-15 coherent variations based on a prompt' and lists concrete use cases (A/B testing, storyboards, product variations, exploration, social media). It also includes a strong when-not directive with an explicit alternative: 'This tool WILL TIMEOUT on Claude.ai... Use seedream_submit instead.'

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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