Colour Memory

by app.railway.up.colour-memory-api-production

Personal Colour Analysis — Find Your Colours

image_personal

Read-only

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

Name	Required	Description	Default
`name`	No	Optional: person's name for the report e.g. 'Sarah'
`image_url`	No	URL of a portrait photo hosted online. Easier than base64 for MCP use. Either image_url or image_base64 required.
`media_type`	No	Image MIME type e.g. 'image/jpeg'	image/jpeg
`image_base64`	No	Base64 encoded portrait photo (JPEG or PNG). Face should be clearly visible in natural light. Either image_base64 or image_url required.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedJun 20, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "description": "Structured JSON response. Shape varies by tool; most return an 'ok' boolean plus a 'result' or 'results' field with the tool's data payload.",
+  "type": "object"
+}

Changed1 schema field changedJun 17, 2026

changedOutput schema / (root)

Previous value: -{
-  "properties": {
-    "error": {
-      "type": "string"
-    },
-    "ok": {
-      "type": "boolean"
-    },
-    "result": {
-      "type": [
-        "object",
-        "array",
-        "string",
-        "null"
-      ]
-    }
-  },
-  "type": "object"
-}New value: +null

Changed3 schema fields changedJun 3, 2026

changedInput schema / properties / image_base64 / description

Previous value: -"Base64 encoded portrait photo (JPEG or PNG). Face should be clearly visible in natural light."New value: +"Base64 encoded portrait photo (JPEG or PNG). Face should be clearly visible in natural light. Either image_base64 or image_url required."

addedInput schema / properties / image_url

Added value: +{
+  "description": "URL of a portrait photo hosted online. Easier than base64 for MCP use. Either image_url or image_base64 required.",
+  "type": "string"
+}

changedInput schema / required

Previous value: -[
-  "image_base64"
-]New value: +[]

AddedJun 2, 2026

TDQS

A4.3/5.0

Behavior4/5

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

The annotation readOnlyHint=true already discloses the read-only nature. The description adds valuable behavioral context: the photo is never stored, the analysis uses Claude Vision, and matching uses CIEDE2000 perceptual distance. It does not cover failure modes or limitations, but the combination of annotation and description provides a solid behavioral profile.

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 front-loaded with the core purpose, then systematically expands into outputs, method, privacy, and an example. Each sentence earns its place without redundancy or bloat, making it well-structured and highly scannable.

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 tool has an output schema (present, though not shown), so the description does not need to detail return values. The description covers the input type, analysis dimensions, output highlights, underlying technique, and privacy guarantee, making it complete for an agent to understand when and how to invoke the tool.

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

Parameters3/5

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

The input schema covers all four parameters with descriptions, including the conditional requirement that either image_url or image_base64 is needed. The description does not add parameter-specific details beyond what the schema states, so the baseline of 3 applies given the high schema coverage.

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 a clear action and object: 'Upload a portrait photo and receive a full personal colour analysis.' It then enumerates specific outputs (seasonal type, colour depth, undertone) and distinctive features (archive colours with provenance, colours to avoid), which clearly distinguishes it from sibling tools like image_palette or palette_extract. The concrete example further anchors the tool's purpose.

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

Usage Guidelines4/5

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

The description establishes the tool's context explicitly (personal colour analysis from a portrait photo) and includes an illustrative example, making it obvious when to use it. However, it does not name alternative tools or state when not to use it, though the uniqueness of 'personal' analysis makes the usage guidance sufficiently clear.

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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