iLook Face Analysis

Analyze color palette

analyze_color_palette

Read-only

Returns style-oriented palette, contrast, and lighting cues.

Input Schema

TableJSON Schema

Name	Required	Description
`mime_type`	Yes	Image media type. Supported formats are JPEG, PNG, and WebP.
`image_base64`	Yes	Base64-encoded JPEG, PNG, or WebP bytes, without a data URL prefix.
`adult_and_authorized`	Yes	Confirms the pictured person is an adult and the submission is lawful.

Output Schema

TableJSON Schema

Name	Required	Description
`tool`	Yes	Focused measurement key.
`error`	No	Present when the focused analysis fails.
`model`	Yes	Model version used for visible analysis.
`phi_score`	No	Optional Golden Ratio context when relevant.
`disclaimer`	Yes	Required responsible-use disclaimer.
`face_shape`	No	Optional face-shape context when relevant.
`analysis_id`	Yes	Unique analysis identifier.
`measurement`	Yes	Score, label, analysis, and tool-specific detail rows.
`rubric_version`	Yes	Versioned iLook scoring rubric.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

AddedAug 21, 2026
RemovedAug 20, 2026

Changed2 schema fields changedAug 19, 2026

addedInput schema / properties / mime_type / description

Added value: +"Image media type. Supported formats are JPEG, PNG, and WebP."

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "properties": {
+    "analysis_id": {
+      "description": "Unique analysis identifier.",
+      "type": "string"
+    },
+    "disclaimer": {
+      "description": "Required responsible-use disclaimer.",
+      "type": "string"
+    },
+    "error": {
+      "description": "Present when the focused analysis fails.",
+      "properties": {
+        "code": {
+          "description": "Stable iLook error code.",
+          "type": "string"
+        },
+        "message": {
+          "description": "Human-readable failure explanation.",
+          "type": "string"
+        }
+      },
+      "type": "object"
+    },
+    "face_shape": {
+      "description": "Optional face-shape context when relevant.",
+      "type": "object"
+    },
+    "measurement": {
+      "description": "Score, label, analysis, and tool-specific detail rows.",
+      "type": "object"
+    },
+    "model": {
+      "description": "Model version used for visible analysis.",
+      "type": "string"
+    },
+    "phi_score": {
+      "description": "Optional Golden Ratio context when relevant.",
+      "type": "object"
+    },
+    "rubric_version": {
+      "description": "Versioned iLook scoring rubric.",
+      "type": "string"
+    },
+    "tool": {
+      "description": "Focused measurement key.",
+      "type": "string"
+    }
+  },
+  "required": [
+    "analysis_id",
+    "rubric_version",
+    "model",
+    "tool",
+    "measurement",
+    "disclaimer"
+  ],
+  "type": "object"
+}

AddedAug 18, 2026

TDQS

B3.4/5.0

Behavior3/5

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

Annotations already establish read-only, non-destructive, and open-world behavior. The description adds that the tool returns 'palette, contrast, and lighting cues,' which is useful context. However, it does not elaborate on edge cases, required inputs (e.g., that an image is needed), or any side effects, leaving room for improvement.

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?

The description is a single, compact sentence that is easy to scan and front-loads the action. While it is concise, the phrase 'style-oriented' is slightly ambiguous, though this does not detract from overall brevity. It is appropriately brief without being under-specified.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that an output schema is available and annotations are provided, the description does not need to detail return structures. However, the description does not clarify whether this tool operates on general images or specifically on faces in the context of the other tools, which is a minor gap. It is adequate but not highly informative.

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?

With 100% schema coverage, the baseline is 3 per the rubric. The description does not mention any parameters, so it neither adds nor detracts from the schema's own descriptions. Since the schema clearly documents all three required parameters, a neutral score is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Returns style-oriented palette, contrast, and lighting cues' uses a specific verb ('Returns') and describes a clear resource. It distinguishes itself from sibling tools, which all focus on facial features, by pointing to color/lighting analysis. However, 'style-oriented' is somewhat vague and could be clarified.

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 only implies usage; no explicit guidance is given on when to use this tool vs. its siblings or how it fits into a broader workflow. Since the tool name and context (among facial analysis tools) suggest it's for color/palette analysis, usage is implied rather than stated, fitting a score of 3.

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

All iLook Face Analysis tools Inspect

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TDQS

A3.8/5.0

Disambiguation4/5

Most tools have distinct purposes (e.g., age, skin, symmetry), but 'score_face' overlaps with several others (face shape, golden ratio, attractiveness) and could be confused with the individual analyzers. Descriptions are clear enough to differentiate, but the aggregation tool introduces some ambiguity.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with lowercase and underscores (analyze_*, detect_*, calculate_*, etc.). The verb varies but the format is uniform and readable.

Tool Count5/5

14 tools is a well-scoped number for a face analysis server—not too few to be trivial, not too many to be overwhelming. Each tool covers a specific metric or feature.

Completeness5/5

The toolset covers a broad range of facial analysis dimensions (age, symmetry, ratios, skin, eye shape, hairstyle, jawline, attractiveness, color palette, golden ratio, PSL) and appears to provide a full suite for the intended domain.

Resources

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