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surewht
by surewht

color_analysis

Analyze RGB channel correlations, saturation, and entropy in an image to detect AI-generated content by revealing unnatural color patterns.

Instructions

Analyze color channel correlations, saturation, and entropy for AI detection.

Based on CVPR 2025 "Secret Lies in Color" — AI-generated images show unusual RGB channel correlations, gradient patterns, and saturation distributions compared to natural photographs.

Args: image_path: Absolute path to the image file.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
image_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description must carry the full behavioral burden. It discloses the analytical method and its theoretical basis, but says nothing about cost, determinism, supported formats, or failure behavior for unreadable paths. Since the tool is an analysis operation, the method disclosure is real value, but operational traits are absent.

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?

Two short blocks, front-loaded with what gets analyzed before the research citation and the argument note. Slightly more prose than strictly necessary in the citation sentence, but every part maps to purpose or parameter guidance.

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?

An output schema exists, so return values need not be explained, and the single required parameter is documented with its absolute-path constraint. What remains missing is routing guidance relative to the six sibling forensic tools, which matters given how many overlapping analyses are offered.

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 description coverage is 0%, so the schema only names the parameter as 'Image Path'. The description compensates meaningfully by specifying it must be an absolute path to the image file, which is exactly the kind of constraint the schema omits. It stops short of listing supported extensions or size limits.

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 names a specific analytical action (channel correlations, saturation, entropy) on a specific resource (the image at image_path) and frames it against a named use case (AI detection). Against siblings like noise_map, frequency_analysis, and spectral_decay_analysis, the color-domain scope makes it immediately distinguishable without opening any schema.

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 states a purpose ('for AI detection') and cites the underlying CVPR 2025 research, which implies when it's useful, but it never states when to prefer this over noise_map, error_level_analysis, or frequency_analysis, nor any precondition such as image format. Usage is inferred rather than prescribed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.