VerifEye

ai.realeyes由 realeyes.ai 发布官方发布者v1.1.1更新于 Oct 24, 2026

VerifEye Face API: Identify (apparent age, gender, emotions, attention), Onboard and Compare faces.

已验证Streamable HTTP可网页运行安全与监控AI 与机器学习

概览

AI 生成的概览

Realeyes 远程 VerifEye 服务器，让助手分析人脸的表观年龄、性别、情绪与注意力，并录入、比对和管理人脸集合。

可以做什么
  • 分析图片中人脸的表观年龄、性别、瞬时情绪与注意力，可指定集合进行识别
  • 将人脸录入集合，并把新图片与某个已录入人脸进行比对
  • 列出、创建、修改描述、重置和删除集合
  • 按自定义标识查找已录入人脸，添加或移除该标识，以及删除人脸
  • 获取短期有效的上传链接以提交图片
适用场景
适合希望助手直接通过 VerifEye Face API 完成人脸分析或人脸比对、而不自行编写集成代码的场景，例如根据照片估算人群属性，或检查某访客是否已在集合中登记。
运行要求
使用文档中给出的 VerifEye 远程地址，通过 Streamable HTTP 连接，无需本地安装。需要 VerifEye Developer Console 账号，以及支持 OAuth 2.1（授权码加 PKCE）远程服务器的 MCP 客户端；部分客户端需要预注册的客户端 ID。图片须以公开的 JPEG 或 PNG 链接提供，或通过上传链接工具上传。除非指定美国区域，每次调用默认在欧盟区域执行。
安装前请注意
会处理人脸生物识别数据：需自行取得知情同意，并遵守 GDPR、BIPA、CCPA 等隐私与生物识别法规。录入会保存持久的人脸模板；删除人脸、重置或删除集合均不可撤销。年龄与性别只是估算，比对结果只是相似度证据，并非身份核验。VerifEye 会自行抓取你提供的图片链接，建议使用短期有效的预签名链接。调用以你的账号执行并计入账号用量限额。

安装

在 SourceWeft 中

  1. 打开 控制台中的 VerifEye，将其添加到工作区。
  2. 连接 所需的 凭据。它们会被私密存储，绝不会在公开页面上展示。
  3. 为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/ai.realeyes%2Fverifeye/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。 客户端会在首次连接时完成 OAuth 登录。

{
  "mcpServers": {
    "verifeye": {
      "type": "http",
      "url": "https://verifeye-mcp-server.realeyes.ai/mcp"
    }
  }
}

README

VerifEye MCP Server

The VerifEye MCP Server is a remotely hosted Model Context Protocol (MCP) server from Realeyes. It exposes the VerifEye Face API as tools an AI assistant can call: analyze a face for apparent age and gender plus momentary emotions and attention (Identify), onboard a face so the same person is identified later (Onboard), compare an image against one onboarded face (Compare), and manage the collections onboarded faces live in.

The server is hosted by Realeyes, so there is nothing to install or run. This repository holds its public listing metadata for MCP registries: server.json.

Full documentation: https://verifeye-docs.realeyes.ai/verifeye-apis/mcp-server/

Server URL

https://verifeye-mcp-server.realeyes.ai/mcp

Transport: Streamable HTTP. This is the server's only address. If a registry or directory lists a different one, do not connect to it, and contact us.

Requirements

Authentication

The server uses OAuth 2.1 (authorization code with PKCE). When you connect, a VerifEye sign-in and consent screen opens in your browser. You never put an API key into your MCP client: your account's API key is resolved on the server side and is never exposed to the client.

The server offers no dynamic client registration. ChatGPT, VS Code and Codex identify themselves with a Client ID Metadata Document, so there is nothing to enter. Claude, Claude Code, Cursor and the Gemini CLI use a pre-registered client ID.

Connecting a client

Step-by-step setup for Claude Code, Claude, ChatGPT, VS Code, Cursor, the Gemini CLI and Codex, including the client ID each one needs, is in the documentation: Connecting a client.

For example, in VS Code add .vscode/mcp.json to your project root:

json
{  "servers": {    "verifeye": { "type": "http", "url": "https://verifeye-mcp-server.realeyes.ai/mcp" }  }}

Start the server from the MCP: List Servers command, then sign in when the browser opens.

Using the server

Once the server is connected and you have signed in, ask in plain language and the assistant picks the tools.

Give the image as a link. The image tools take a public URL of a JPEG or PNG image, which VerifEye fetches itself. An image attached to the conversation cannot be passed to a tool, so put the image somewhere reachable and give the assistant that link. Prefer a pre-signed link that expires in minutes to a durably public URL. Where the server offers verifeye_get_upload_url and your client can make HTTPS requests of its own, the client can upload the image instead. Image requirements: documentation.

Choose the region. Every call runs in the EU (eu) unless you ask for the US (us). Collections are per region, so ask for the same region each time you work with a collection.

Example prompts

The image links are placeholders: use a link to your own image. Prompts 2 to 5 build on each other and use the collection and the face that prompt 2 creates.

#PromptWhat happens
1"Estimate the apparent age and gender of the face in https://example.com/photos/visitor.jpg."verifeye_identify_face analyzes the face and returns the estimates. No face or image is stored.
2"Create a VerifEye collection called front-desk-visitors, then onboard the face in https://example.com/photos/visitor-1042.jpg into it with the external key visitor-1042."verifeye_create_collection creates the collection, and verifeye_onboard_face stores the face template with that key. Onboarding stores biometric data: see Biometric data and consent.
3"Is the person in https://example.com/photos/today.jpg already in my front-desk-visitors collection?"verifeye_identify_face with that collection answers Existing, with the faceId and the key, when the photo shows the person onboarded in prompt 2, and NotExisting for anyone else. Nothing is onboarded.
4"Compare the face in https://example.com/photos/check-in.jpg with visitor-1042 in front-desk-visitors."verifeye_get_face_by_key finds the faceId, and verifeye_compare_face returns the similarity and VerifEye's isMatch verdict.
5"Delete visitor-1042's face from front-desk-visitors."verifeye_get_face_by_key finds the face, and verifeye_delete_face destroys its template. This cannot be undone.

Many clients ask you to approve a call before it runs, for example before a face is onboarded or deleted.

Tools

Every tool takes region (eu or us, eu by default), which selects where the image is processed and where a collection lives. Collections are per region: one created in eu is not visible from us.

ToolWhat it doesStores or destroys data
verifeye_identify_faceAnalyzes the face(s) in an image, and identifies them against a collection if you name oneNo
verifeye_onboard_faceOnboards the face(s) in an image into a collection so the person is identified laterStores a face template
verifeye_compare_faceCompares the face(s) in an image against one onboarded faceNo
verifeye_delete_faceDeletes one onboarded faceIrreversible
verifeye_list_collectionsLists the collections on the account, or checks whether one existsNo
verifeye_create_collectionCreates an empty collectionNo
verifeye_set_collection_descriptionReplaces a collection's descriptionNo
verifeye_reset_collectionDeletes every face in a collection, keeping the collectionIrreversible
verifeye_delete_collectionDeletes a collection and every face in itIrreversible
verifeye_get_face_by_keyFinds an onboarded face by your own identifierNo
verifeye_set_face_keyAttaches your own identifier to an onboarded faceNo
verifeye_delete_face_keyRemoves your identifier from an onboarded faceLoses the way of finding the face
verifeye_get_upload_urlHands back a short-lived form for uploading one imageNo

Parameters, results and error codes for each tool are in the documentation.

Biometric data and consent

These tools process facial (biometric) data. You are responsible for obtaining valid, informed consent before a call runs, and for meeting the privacy and biometric-data regulations that apply to the people in your images (for example GDPR, BIPA and CCPA).

  • verifeye_onboard_face stores a face template that persists until it is deleted.
  • verifeye_delete_face, verifeye_reset_collection and verifeye_delete_collection destroy face templates permanently.
  • Age is an apparent-age estimate, not a legal proof of age. Gender is a demographic estimate, not a self-declared identity attribute. A face match is evidence of similarity, not an identity check on its own.
  • Tool calls run as your VerifEye account and count against your account's usage limits.

Links

来源：README.md，提交 40792e5

工具

0
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版本历史

2
  1. v1.1.1最新Oct 16, 2026
  2. v1.1.0Oct 11, 2026

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