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face_compare

Compare two face photos and receive a similarity score to verify identity for onboarding, duplicate checks, and selfie-to-ID matching.

Instructions

Compare two face photos and return a similarity score from 0 to 100. Best for customer onboarding, duplicate-account checks, selfie-to-ID matching, and agent workflows that need a same-person decision. Input supports file path, HTTPS URL, data URL, or base64. Suggested business rule: score >=80 means high-confidence same person, but final thresholds should be calibrated by scenario.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "photo1": "<photo1>",
      "photo2": "<photo2>"
    },
    "name": "face_compare"
  }
}

Input Schema

Name	Required	Description	Default
`photo1`	Yes	First face photo: local file path, HTTPS URL, data URL, or base64.
`photo2`	Yes	Second face photo: local file path, HTTPS URL, data URL, or base64.
`sourcePhotoType`	No	Reference photo type: 1=watermarked ID photo, 2=HD photo. Default: 2.	2

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.17Jul 28, 2026

TDQS

A4/5.0

Behavior3/5

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

With no annotations provided, the description covers input formats, suggested thresholds, and calibration need. However, it does not disclose potential side effects, auth requirements, or behavior on failure (e.g., no face found). Adequate but not exhaustive.

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 two sentences with no wasted words. It front-loads the core function, then lists use cases, input support, and a business rule. Highly efficient.

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?

Given no output schema, the description mentions the similarity score range and use cases. It provides input format details and threshold guidance. Missing error handling or response structure, but overall sufficient for a simple comparison tool.

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 coverage is 100%, so the schema already describes each parameter. The description restates input formats but does not add new parameter-level details (e.g., does not elaborate on sourcePhotoType enum). Baseline 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 clearly states the tool compares two face photos and returns a similarity score from 0 to 100. It specifies the resource (face photos) and action (compare), and distinguishes from siblings like liveness detection or OCR tools.

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

Usage Guidelines4/5

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

The description provides explicit use cases (customer onboarding, duplicate-account checks, selfie-to-ID matching) and a suggested business rule for decision thresholds. It does not mention when not to use or list alternative tools, but the context is clear.

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

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