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Personal Colour Analysis — Find Your Colours

image_personal
Read-only

Upload a portrait photo to receive a personal colour analysis. Get your seasonal type, undertone, and a curated palette of archive colours with historical context.

Instructions

Upload a portrait photo and receive a full personal colour analysis. Determines your seasonal type (Spring, Summer, Autumn, or Winter), colour depth (light, medium, or deep), and undertone (warm, cool, or neutral). Returns a curated palette of archive colours that genuinely suit you — each with full historical provenance and cultural context — plus colours to avoid. Uses Claude Vision for skin, hair, and eye analysis, then matches to the archive by CIEDE2000 perceptual distance. The photo is never stored. Example: a Deep Winter might wear Ottoman Carbon Ink while a True Spring suits Kogi Mango.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
image_base64NoBase64 encoded portrait photo (JPEG or PNG). Face should be clearly visible in natural light. Either image_base64 or image_url required.
image_urlNoURL of a portrait photo hosted online. Easier than base64 for MCP use. Either image_url or image_base64 required.
media_typeNoImage MIME type e.g. 'image/jpeg'image/jpeg
nameNoOptional: person's name for the report e.g. 'Sarah'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNo
resultNo
errorNo
Behavior5/5

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

The description reveals that the photo is never stored, uses Claude Vision for analysis, and matches by CIEDE2000 perceptual distance. These details go beyond the readOnlyHint annotation, which already indicates no destructive behavior.

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 two sentences with an example, efficiently conveying purpose, process, and an illustrative case. No extraneous information.

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?

Given the presence of an output schema (not shown but known), the description adequately covers the input, process, and return elements (seasonal type, depth, undertone, palette). All parameters are addressed.

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?

All 4 parameters have schema descriptions (100% coverage). The description adds extra guidance: 'Face should be clearly visible in natural light' and explains base64 vs URL usage, adding meaningful context beyond the schema.

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: upload a portrait photo for personal colour analysis, determining seasonal type, colour depth, undertone, and providing a palette. It distinguishes from sibling tools like image_palette or colour_forensics by focusing on personal analysis.

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?

The description explicitly states when to use: when you have a portrait photo and want a full personal colour analysis. It provides an example (Deep Winter, True Spring) making context clear. While it doesn't list alternatives, the purpose is sufficiently specific.

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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