MCP Server

Overview

The VerifEye MCP Server is a remotely-hosted Model Context Protocol (MCP) server that exposes VerifEye's demographic-estimation capabilities — apparent age and gender — as tools an AI assistant (such as Claude) can call over Streamable HTTP.

The MCP server enables you to:

Estimate apparent age and detect gender for faces in an image, directly from an MCP client
Authenticate once via OAuth — your client never sends an API key
Choose where each image is processed — in the EU or the US — on every call (data residency)

The MCP server is a thin, OAuth-authenticated layer over the Demographic Estimation API. Your account's API key is resolved server-side from your login — the client never sees or sends it.

Prerequisites

A VerifEye Developer Console account — sign in at the VerifEye Developer Console.
An MCP-capable client that supports remote MCP servers over OAuth (for example, Claude Code, claude.ai, or the Gemini CLI).

Server URL

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

The server has a single global endpoint. You choose where each image is processed — in the EU or the US — on every call via the region parameter (eu/us) on each tool, for data residency and compliance. See Available tools.

Authentication

The MCP server uses OAuth 2.1 (authorization code with PKCE). Authentication happens through a branded VerifEye login and consent screen — you never put an API key into your MCP client.

Authorization flow

Your MCP client reads the MCP server's protected-resource metadata and discovers the VerifEye authorization server.
The client starts an OAuth 2.1 authorization-code flow (with PKCE) and opens the VerifEye login in your browser.
You sign in with your VerifEye Developer Console account and approve the Allow / Deny consent screen.
The authorization server returns a short-lived, signed access token scoped to the MCP server. Your client refreshes it automatically until the refresh window lapses.
The client calls the MCP server with that token. The server resolves your account's API key on the server side and calls VerifEye on your behalf — the key is never exposed to the client.

Connecting a client

Claude Code (CLI)

claude mcp add --scope user --transport http \
  --client-id verifeye-mcp-claude --callback-port 8080 \
  verifeye https://verifeye-mcp-server.realeyes.ai/mcp

Then run /mcp in Claude Code, select verifeye, and authenticate — the branded VerifEye login opens in your browser. Approve it to connect; the tools become available to the assistant.

claude.ai (connector)

Add the same URL — https://verifeye-mcp-server.realeyes.ai/mcp — through the Connectors UI. The server uses pre-registered OAuth clients and does not support automatic client registration, so edit the connector and set the OAuth Client ID to verifeye-mcp-claude. Then complete the VerifEye login and consent.

Gemini CLI

The Gemini CLI connects over Streamable HTTP with a pre-registered OAuth client. Add the server to ~/.gemini/settings.json (merge with any keys already there):

{
  "mcpServers": {
    "verifeye": {
      "httpUrl": "https://verifeye-mcp-server.realeyes.ai/mcp",
      "authProviderType": "dynamic_discovery",
      "oauth": {
        "clientId": "verifeye-mcp-gemini",
        "redirectUri": "http://localhost:7777/oauth/callback",
        "scopes": ["verifeye"]
      }
    }
  }
}

Because the server does not support automatic client registration, clientId must be set to verifeye-mcp-gemini, and redirectUri must be pinned to port 7777 — the CLI otherwise picks a random port, which the server rejects. Then run /mcp auth verifeye in the CLI to complete the VerifEye login and consent; /mcp then lists the available VerifEye tools.

Available tools

verifeye_estimate_age

Estimates the apparent age of the face(s) in an image.

Input parameters:

Parameter	Type	Required	Default	Description
`imageUrl`	string	No	—	Public URL of a JPEG or PNG image. Provide either `imageUrl` or `imageBase64`.
`imageBase64`	string	No	—	Base64-encoded JPEG or PNG (no `data:` prefix). Used only when `imageUrl` is not set.
`maxFaceCount`	integer	No	`1`	Maximum number of faces to analyze (1–10).
`region`	string	No	`eu`	Data-residency region: `eu` or `us`.

Result:

{
  "region": "eu",
  "faces": [
    {
      "faceIndex": 0,
      "estimatedAge": 27.5,
      "uncertainty": 0.2,
      "reliable": true,
      "faceConfidence": 0.999
    }
  ],
  "unprocessedFaceCount": 0
}

Field	Type	Description
`region`	string	The region the request was processed in.
`faces[].faceIndex`	integer	Zero-based index of the face in the result.
`faces[].estimatedAge`	number	Estimated apparent age.
`faces[].uncertainty`	number	Uncertainty of the estimate (lower is better).
`faces[].reliable`	boolean	`false` when the estimate is unreliable (uncertainty above 1.0). Prefer this flag over the raw age.
`faces[].faceConfidence`	number	Face-detection confidence (0.0–1.0).
`unprocessedFaceCount`	integer	Faces detected but not analyzed (beyond `maxFaceCount`).

verifeye_detect_gender

Detects the apparent gender (Male or Female) of the face(s) in an image.