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image_analyze_product

Extract dominant colors from a product image or video frame and optionally generate an AI-powered product description for visual analysis.

Instructions

Analyze a product image or video frame — extract colors and optionally generate AI description.

Extracts dominant colors from an image. Optionally uses Claude Vision to generate a natural language description of the product.

Args: image_path: Absolute path to the image or video file. If video, extracts a representative frame. use_ai: If True, use Claude Vision to generate a description (requires ANTHROPIC_API_KEY). n_colors: Number of dominant colors to extract (default 5).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
image_pathYes
use_aiNo
n_colorsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description well discloses behavior: extracts dominant colors, optionally uses Claude Vision (requires API key), handles video by extracting a frame, and defaults n_colors to 5. No side effects or destructive actions mentioned, which is appropriate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise with a clear summary and structured Args section. A bit verbose but every sentence adds value; could be slightly tighter but well-organized.

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?

Covers main behaviors and parameters; output schema handles return values. Lacks explicit mention of return format (e.g., colors as hex codes) but sufficient given output schema exists. No guidance on prerequisites like image path existence.

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

Parameters5/5

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

Schema coverage is 0%, but description adds full meaning: image_path (absolute path, video handling), use_ai (requires API key), n_colors (default 5). Each parameter explained beyond schema titles/types.

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 it analyzes a product image or video frame to extract colors and optionally generate an AI description. It distinguishes from siblings like image_extract_colors and image_generate_palette by being product-focused and supporting video frames.

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?

No explicit guidance on when to use vs alternatives (e.g., image_extract_colors for simple color extraction). The description implies product context but doesn't provide when-not or alternative references.

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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