iLook Face Analysis

Calculate facial ratios

calculate_facial_ratios

Read-only

Returns visible face-ratio and spacing cues from one photo.

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

A3.5/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, so the read-only nature is covered. The description adds minimal behavioral context by specifying 'visible' cues, implying that occluded or obscured facial features are not analyzed. However, it does not disclose potential limitations, error conditions, or edge cases, and adds little beyond the annotations.

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. It states exactly what the tool returns and the input context, making it extremely concise and easy to parse.

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 the output schema exists, the return values are already documented. However, the description lacks guidance on intended use cases or how it differs from overlapping sibling tools (e.g., 'analyze_golden_ratio_face'). The term 'ratio and spacing cues' is somewhat vague, and without usage context, the description feels incomplete for a tool with 13 siblings. It is adequate but not thorough.

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% (all three parameters have descriptions in the schema). The tool description itself adds no specific parameter details; it only says 'from one photo', which is consistent with the image_base64 parameter. The schema carries the parameter semantics, so a baseline of 3 is appropriate.

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 uses a specific verb ('Returns') with a clear resource ('visible face-ratio and spacing cues') and a source ('from one photo'). It clearly distinguishes from sibling tools like 'analyze_face_shape' or 'analyze_golden_ratio_face' by focusing on general ratios and spacing cues rather than specific shape or proportional analysis.

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

Usage Guidelines2/5

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

No when-to-use guidance or mention of alternatives is provided. Given the large set of sibling analysis tools (e.g., 'analyze_golden_ratio_face', 'rate_jawline'), the description does not help the agent decide when this tool is the right choice over others. There is no 'use this for...' or 'instead of X' context.

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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