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Glama

iLook Face Analysis

by fit.ilook

Rate jawline

rate_jawline
Read-only

Returns visible jawline definition, balance, and lower-face cues.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
mime_typeYesImage media type. Supported formats are JPEG, PNG, and WebP.
image_base64YesBase64-encoded JPEG, PNG, or WebP bytes, without a data URL prefix.
adult_and_authorizedYesConfirms the pictured person is an adult and the submission is lawful.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolYesFocused measurement key.
errorNoPresent when the focused analysis fails.
modelYesModel version used for visible analysis.
phi_scoreNoOptional Golden Ratio context when relevant.
disclaimerYesRequired responsible-use disclaimer.
face_shapeNoOptional face-shape context when relevant.
analysis_idYesUnique analysis identifier.
measurementYesScore, label, analysis, and tool-specific detail rows.
rubric_versionYesVersioned iLook scoring rubric.

Schema Changelog

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

  1. Added
  2. Removed
  3. Changed2 schema fields changed
    • 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"
+}
  4. Added

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it's safe and non-mutating. The description adds that it returns 'visible jawline definition, balance, and lower-face cues', which gives some insight into output nature. However, it does not explain any edge cases (e.g., what happens if image is ambiguous, whether it requires specific pose) or rate limits. Since annotations cover the safety profile, a 3 is appropriate.

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 sentence, immediately states the action (returns) and the subject (jawline definition, balance, cues). It is front-loaded, avoids fluff, and every word adds value. No redundancy or unnecessary detail.

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?

For a simple read-only tool with full schema coverage and an output schema (as indicated by has_output_schema: true), the description is adequate. It tells what it returns but doesn't mention what the output schema contains (e.g., scores, attributes). It also doesn't clarify whether 'visible' implies something about camera angle or lighting. It is functional but could be more informative about the expected output structure or any constraints, given the tool's purpose.

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%, so all three parameters (mime_type, image_base64, adult_and_authorized) are already described in the schema. The description does not add extra meaning about parameters, such as constraints or typical usage. According to the rubric, with high coverage, baseline is 3, and there is no added value beyond that.

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 clearly states the tool returns 'visible jawline definition, balance, and lower-face cues', which is a specific verb (returns) and resource (jawline). It distinguishes itself from sibling tools like analyze_face_shape or analyze_facial_symmetry by focusing on jawline-specific attributes. Not a perfect 5 because it doesn't explicitly frame it as a rating or score, but the purpose is clear.

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?

The description provides no guidance on when to use this tool versus alternatives like analyze_face_shape or score_face. There is no mention of prerequisites (e.g., image quality, frontal view) or exclusions. The only context is the tool name and return description, which implies jawline analysis but does not help an agent decide between this and siblings.

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 toolsInspect
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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.

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