iLook Face Analysis

Analyze face shape

analyze_face_shape

Read-only

Returns face shape, runner-up shape, outline cues, and styling direction.

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?

The annotations declare readOnlyHint=true and destructiveHint=false, so the agent knows it's a safe read operation. The description adds that it returns outline cues and styling direction, which are non-obvious outputs, but it does not disclose any additional behavioral traits such as dependency on image quality or that a clear frontal view is needed.

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 sentence with no wasted words. It is concise and structured to state the output clearly, though it could benefit from mentioning typical use cases, but overall it is appropriately sized.

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?

The tool has an output schema and good parameter schema, but the description lacks context on when to use it versus siblings. It mentions the key outputs, but doesn't explain the significance of face shape analysis or any prerequisites, such as needing a clear photo. Overall it is adequate but leaves some gaps.

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 schema description coverage is 100%, so the parameters are well-documented in the schema. The description adds no additional parameter meaning beyond what the schema provides, but given the high coverage, a score of 3 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 states the tool returns face shape, runner-up shape, outline cues, and styling direction. This clearly identifies the purpose, but it does not explicitly distinguish it from sibling tools like analyze_golden_ratio_face or analyze_facial_symmetry, which also analyze facial features.

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 is for analyzing face shape, but it provides no explicit guidance on when to use it over alternatives like analyze_facial_symmetry or calculate_facial_ratios. The context from the name and siblings suggests a use case, but no exclusions or alternatives are mentioned.

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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