How to Use the Yitu Technology / 依图科技 MCP in Pydantic AI

Q: What kind of data does Yitu Technology / 依图科技 handle regarding privacy?

It handles biometric face data, which is crucial for addfacetorepo and comparefaces. Because the output is strictly typed, you know exactly what fields contain this sensitive information.

Q: If I need to search a database using Yitu Technology / 依图科技 with Pydantic AI, how does it work?

You call searchfaceinrepo. The result is immediately validated by Pydantic. You get a structured list of top matches and their confidence scores—no messy, unpredictable data to deal with.

Q: Can I perform general content checks using Yitu Technology / 依图科技?

Yes, use moderateimage for generalized image moderation. The toolset validates the output of this function, confirming that the agent correctly handled the content filtering process.

Q: What if I need to delete a facial repository using Yitu Technology / 依图科技?

You call deletefacerepo. Since your workflow is type-safe, you're guaranteed that the deletion operation was successful and that no dependent records are left hanging.

Run validated computer vision tools—including face detection and OCR—from your Pydantic AI workflows.

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Connect Yitu Technology / 依图科技 MCP to Pydantic AI

Create your Vinkius account to connect Yitu Technology / 依图科技 to Pydantic AI 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 Pydantic AI

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Detect Faces and Check Liveness

The toolset starts by detecting faces in an image using `detect_face`. You can then perform advanced checks: use `detect_active_liveness` for real-time actions, or `detect_silent_liveness` for anti-spoofing on photos. Because your workflow is typed, the Pydantic models ensure that any output from these tools—like the pass/fail metrics—is correctly validated before your agent proceeds.

Manage and Compare Identities

Need to build a new identity set? Call `create_face_repo` first. Then, you can register faces with `add_face_to_repo`. If an identity is compromised or retired, you use `remove_face_from_repo`. For validation, the agent uses `compare_faces`, which verifies if two specific facial inputs match each other.

Search and Extract Data Points

You can run a broad search for an identity using `search_face_in_repo`. The Pydantic validation guarantees that the resulting list of top matches has the expected structure. Beyond faces, use `ocr_id_card` to extract text from documents. This structured data is then validated against your models, preventing hallucinated fields in your final output.

Setup guide

Set up Yitu Technology / 依图科技 MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "yitu-technology-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Yitu Technology / 依图科技 tools.",
)

result = await agent.run("List recent Yitu Technology / 依图科技 transactions")
print(result.output)

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Common questions about Yitu Technology / 依图科技 MCP in Pydantic AI

Every MCP response—whether it's a list of matches or an OCR result—is checked against your defined Pydantic models at runtime. This means the agent fails loud and clear if the API returns unexpected data.

It handles biometric face data, which is crucial for `add_face_to_repo` and `compare_faces`. Because the output is strictly typed, you know exactly what fields contain this sensitive information.

You call `search_face_in_repo`. The result is immediately validated by Pydantic. You get a structured list of top matches and their confidence scores—no messy, unpredictable data to deal with.

Yes, use `moderate_image` for generalized image moderation. The toolset validates the output of this function, confirming that the agent correctly handled the content filtering process.

You call `delete_face_repo`. Since your workflow is type-safe, you're guaranteed that the deletion operation was successful and that no dependent records are left hanging.

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