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CCCpan

Data Verify MCP Server

by CCCpan

face_compare

Compare two face photos to verify identity by checking if they belong to the same person. Returns similarity score and confidence level.

Instructions

Compare two face photos to determine if they belong to the same person (人脸比对/人像对比). Returns similarity score (0-100), same-person judgment, confidence level, and face quality scores. Use for identity verification with photo, face authentication, or liveness detection scenarios. Images must be base64 encoded. Free tier: 30 requests/day.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
image_base64_1YesFirst face photo in base64 encoding (第一张人脸照片的base64编码)
image_base64_2YesSecond face photo in base64 encoding (第二张人脸照片的base64编码)
Behavior4/5

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

No annotations provided; description fills in by disclosing return data, input requirements, and rate limit. Does not contradict any annotations. Could add details on error handling or precision, but sufficient.

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?

Three sentences, front-loaded with purpose, then details. No unnecessary words. Highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, description covers input, output, use cases, and constraints. Complete for a simple two-parameter tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage with descriptions but they are basic ('first face photo in base64 encoding'). Description adds context of use and requirement, enhancing the schema.

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?

Description clearly states the tool compares two face photos to determine if they belong to the same person, specifies returned data (similarity score, judgment, confidence, quality scores), and lists use cases. Distinct from sibling tools like verify_identity or ocr_recognize.

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?

Explicitly advises use for identity verification, face authentication, and liveness detection. Mentions required base64 encoding and free tier limit. Does not state when not to use, but 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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