iLook Face Analysis

Analyze skin appearance

analyze_skin_appearance

Read-only

Returns appearance-only texture, evenness, shine, 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

A3.8/5.0

Behavior3/5

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

Annotations already indicate readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile is covered. The description adds that results are appearance-only cues, which is useful, but it does not disclose real-world operational behavior such as format concerns, failure modes, or limitations beyond the schema.

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 a single front-loaded sentence with no filler, tautology, or redundant restatement. Every word in the phrase 'appearance-only texture, evenness, shine, and lighting cues' contributes directly to understanding the tool's purpose.

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?

The input schema and output schema cover structural details, and annotations provide safety/read-only context. The description is sufficient for a simple read-only appearance analyzer, but explicit guidance about when to prefer it over sibling face-analysis tools would make the context fuller.

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?

Schema description coverage is 100%, and each parameter, especially image_base64, mime_type, and adult_and_authorized, is already described in the schema. The description adds no extra meaning beyond the schema, so the baseline score of 3 applies.

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 what the tool returns: appearance-only texture, evenness, shine, and lighting cues. It uses a specific verb ('returns') and resource, and the focus on skin appearance distinguishes it from the face-shape, symmetry, and ratio-focused sibling tools.

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 implies the tool should be used for skin-appearance analysis, and 'appearance-only' hints at a scope restriction. However, it does not explicitly state when to use this tool over alternatives or provide exclusions/alternative recommendations, leaving usage guidance mostly implicit.

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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