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image_generate_palette

Extract dominant colors from an image or video frame and generate color harmony palettes using complementary, analogous, triadic, or split-complementary schemes.

Instructions

Generate a color harmony palette from an image or video frame.

Extracts the dominant color and generates harmonious colors based on color theory (complementary, analogous, triadic, split_complementary).

Args: image_path: Absolute path to the image or video file. If video, extracts a representative frame. harmony: Harmony type (complementary, analogous, triadic, split_complementary). n_colors: Number of dominant colors to base palette on (default 5).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
image_pathYes
harmonyNocomplementary
n_colorsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that for video inputs a representative frame is extracted, which is helpful. However, it does not disclose whether the tool modifies files, requires permissions, or has any side effects, leaving gaps in transparency.

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 concise and well-structured. It begins with a clear purpose statement, then provides parameter details in a bulleted style. Every sentence adds value without unnecessary elaboration.

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?

Given the presence of an output schema (not shown), the description does not need to detail return values. It covers inputs, behavior for video handling, and harmony types. However, it could mention constraints like supported image formats or file size limits, which are not addressed.

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?

The schema has no descriptions (0% coverage), so the description's parameter explanations add significant value. It clarifies that image_path is an absolute path, lists the valid harmony types, and explains n_colors. This goes beyond the bare schema types and defaults.

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 purpose: generating a color harmony palette from an image or video frame. It specifies the verb 'generate' and the resource 'palette', and distinguishes it from sibling tools like image_extract_colors by focusing on harmony theory.

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?

The description does not provide guidance on when to use this tool versus alternatives. It lacks context about suitable scenarios, exclusions, or prerequisites, leaving the agent to infer usage from the tool name and purpose alone.

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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