How to Use the Yitu Technology / 依图科技 MCP in Google ADK

Q: Can I manage my facial repositories using Yitu Technology / 依图科技 with the Google ADK?

Absolutely. You can create new repos via createfacerepo and delete old ones using deletefacerepo. The agent manages this lifecycle, giving you full control over your identity storage.

Q: Is the search functionality reliable with Yitu Technology / 依图科技?

Yes. The searchfaceinrepo tool is designed to return top matches using confidence scores, giving you a quantifiable measure of how sure the system is about a match.

Q: What happens if I need to remove an identity from a repository?

You call removefacefromrepo. This tool lets your agent surgically remove specific faces without affecting other identities stored in the same facial repository.

Perform computer vision tasks, including facial recognition and anti-spoofing, using Google ADK agents.

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Connect Yitu Technology / 依图科技 MCP to Google ADK

Create your Vinkius account to connect Yitu Technology / 依图科技 to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Yitu Technology / 依图科技 with Google ADK

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Verify Face Identity & Liveness

The `detect_active_liveness` tool gives you per-action pass/fail status, which is crucial for secure authentication. This verifies that the person interacting with your system is present and real. If you're working with photos or screens, use `detect_silent_liveness`. It reliably detects common spoofing methods like 3D masks, giving you confidence in the image source.

Perform Face Search and Registration

You can register new identities by calling `add_face_to_repo`, building up your secure database. To find a match, run `search_face_in_repo` for top hits with confidence scores. If you need to perform a direct check between two people, the `compare_faces` tool handles that 1:1 comparison efficiently.

Content Moderation and Data Extraction

Before processing any image data, run `moderate_image` for content filtering. This acts as a safety check on incoming visual input. For structured text like ID cards, use `ocr_id_card`. It reliably extracts the necessary text fields from an image so your agent can act on it in BigQuery.

Setup guide

Set up Yitu Technology / 依图科技 MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Yitu Technology / 依图科技 tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Yitu Technology / 依图科技_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Yitu Technology / 依图科技 tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Yitu Technology / 依图科技 MCP in Google ADK

The toolset integrates directly into your LlmAgent, making the MCP Server available for calls. You pass the `McpToolset` to the agent, letting it act on the vision tools when needed.

It handles biometric face data and image content, especially during moderation checks like `moderate_image`. Always ensure your BigQuery schemas are designed to account for this sensitive information.

Absolutely. You can create new repos via `create_face_repo` and delete old ones using `delete_face_repo`. The agent manages this lifecycle, giving you full control over your identity storage.

Yes. The `search_face_in_repo` tool is designed to return top matches using confidence scores, giving you a quantifiable measure of how sure the system is about a match.

You call `remove_face_from_repo`. This tool lets your agent surgically remove specific faces without affecting other identities stored in the same facial repository.

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