Yitu Technology MCP for AI. Verify Identity & Manage Biometric Data
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Yitu Technology connects your AI agent directly to enterprise-grade computer vision. This MCP handles everything from real-time facial detection in images to high-precision identity verification and full repository management.
It lets you build security workflows that verify identities, read ID cards, and manage biometric data through natural conversation.
What your AI can do
Detect active liveness
Verifies if a person is genuinely alive by checking for specific, dynamic actions.
Add face to repo
Registers a new face into an existing facial data repository.
Compare faces
Performs a one-on-one comparison to confirm if two faces belong to the same individual.
The agent finds faces in an image, providing their exact location and attributes.
It compares two specific images to determine if the individuals pictured are the same person.
The agent searches a private collection of stored faces and returns the top matching identities with confidence scores.
It performs advanced liveness detection, verifying that the image or video stream comes from a live person, not a photo or screen.
You can create, list, and delete entire collections of facial data, keeping your identity infrastructure organized.
The agent reads text off an ID card image, pulling out structured information like names or dates.
Ask an AI about this
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Yitu Technology: 12 Biometric Tools
These twelve tools allow your agent to handle every step of the biometric workflow, from initial image analysis to final identity confirmation.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Yitu Technology / 依图科技 on VinkiusDetect Active Liveness
Verifies if a person is genuinely alive by checking for specific, dynamic actions.
Add Face To Repo
Registers a new face into an existing facial data repository.
Compare Faces
Performs a one-on-one comparison to confirm if two faces belong to the same...
Create Face Repo
Establishes an empty, new facial repository for storing identities.
Delete Face Repo
Permanently removes an entire facial data repository and all its contents.
Detect Face
Analyzes an image to locate and map the coordinates of faces within it.
List Repos
Retrieves a list of all facial repositories currently managed by the system.
Moderate Image
Checks an image for inappropriate or restricted content types before processing it...
Ocr Id Card
Extracts text data from a picture of an official identification card.
Remove Face From Repo
Deletes one or more specific faces from a designated repository.
Search Face In Repo
Searches an entire face repository to find matches for a given face, returning...
Detect Silent Liveness
Performs anti-spoofing checks that detect if a photo, screen printout, or mask is being used.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Yitu Technology / 依图科技, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Yitu Technology / 依图科技. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 12 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Checking identities used to be a nightmare of tabs and spreadsheets.
Think about onboarding: you pull up a physical ID, you manually key in the name into one system, then upload a photo to another system, wait for confirmation, and finally copy that unique person ID into your main database. It's clicks, copies, paste, repeat.
With this MCP, your agent handles the entire process. You ask it to onboard staff, and it automatically runs `ocr_id_card` on the image, detects the face, checks for liveness, and adds the profile using `add_face_to_repo`. The result is a single confirmation message.
The Yitu Technology MCP gives you identity verification.
You stop manually comparing visual data across multiple systems. You no longer have to worry about whether the photo matches the ID or if the person is actually present in the image.
Your agent executes these checks instantly and reliably, giving you a definitive pass/fail status without any human intervention.
What your AI can actually do with this
This connector gives your AI agent the ability to run advanced visual intelligence checks on complex datasets. You can move beyond simple image tagging; your agent detects faces in images, verifies if two people pictured belong to the same person using high-precision comparison, and indexes new identities into secure repositories.
It’s ideal for any system that relies on verifying who a person is or ensuring data integrity from physical documents. For example, you can detect faces and then use another tool to pull text from an ID card image in one sequence. The real value comes when you build workflows across platforms; because everything runs through Vinkius, your agent can chain this biometric check with other services—like a messaging platform or a database write—all from a single prompt.
It ensures that the entire flow is secure and auditable.
019d84a1-2c70-718f-a6b8-bc6f9c4bbac4 Here's how it actually works
The bottom line is you tell your AI client what security check you need; Vinkius handles running that complex visual logic securely in the background.
First, subscribe to this MCP and provide your required Yitu AppID (DevId) and APIKey (DevKey).
Second, connect the credentials to your AI client. This ensures all biometric calls run through Vinkius's zero-trust proxy.
Third, prompt your agent with a task—for instance, 'Verify this face against my employee database.' The MCP executes the sequence and returns the match result.
Who is this actually for?
Security architects, compliance officers, and operations engineers who are tired of manually cross-referencing physical IDs or checking biometric logs across multiple siloed dashboards.
They use the MCP to audit access control records by running a full identity check against stored repositories.
They automate checkpoint monitoring, using face detection and liveness checks on incoming feeds.
They manage large-scale identity archives by creating new repositories or removing outdated biometric records via natural language prompts.
What Changes When You Connect
Reduces manual identity checks. Instead of comparing two images manually, you call compare_faces and get an immediate boolean match result.
Handles anti-fraud measures automatically. Use detect_active_liveness or detect_silent_liveness to confirm a person is real before processing any data.
Simplifies record keeping. You can use list_repos to see all your active identity collections and then manage them by calling create_face_repo when needed.
Streamlines document intake. When processing applications, simply run the image through ocr_id_card first; the extracted text is ready for immediate comparison against a face record.
Maintains data hygiene. You can ensure your records are current by running remove_face_from_repo when an employee leaves or updates their details.
See it in action
Onboarding New Staff
A new hire needs access control. The agent first uses ocr_id_card to pull the name and ID number from their physical card, then runs detect_face on a submitted photo, and finally uses add_face_to_repo to enroll them into the 'Active Staff' repository.
Fraud Detection at Checkpoints
A checkpoint camera feed is analyzed. The system runs detect_face, checks for liveness using detect_active_liveness, and then uses search_face_in_repo to verify the identity against high-security logs.
Auditing Data Access
Compliance requires knowing who accessed a record. The agent lists all repositories using list_repos and verifies if specific individuals are present in each one, reporting back on any discrepancies.
The honest tradeoffs
Only checking for face existence
Just running a basic image analysis tool without confirming the subject is real. This fails if someone uses a high-quality printed photo of themselves.
Always pair detect_face with an anti-spoofing check like detect_active_liveness or detect_silent_liveness. Don't just assume the image is valid.
Relying on simple text lookups
Searching for a user by name alone in a database. This is easy to spoof and provides zero biometric confirmation.
After finding an ID, use ocr_id_card to extract structured data, then perform the ultimate validation using compare_faces against a known repository.
Manually managing records
A developer writing boilerplate code to add or remove faces. This is slow and error-prone.
Let your agent handle it. Use add_face_to_repo or remove_face_from_repo via a simple prompt instead of writing complex, multi-step API calls.
When It Fits, When It Doesn't
Use this MCP if the core business requirement involves establishing identity, confirming physical presence, or validating data integrity tied to human subjects. You absolutely need it when verifying access control, processing government IDs, or conducting KYC/AML checks.
Don't use it if your job is purely text generation, simple database querying (e.g., only updating user emails), or analyzing general image content without a focus on faces or biometrics. If you just need to count objects in an image, another tool category works better. This MCP requires the identity verification step; everything else is secondary.
Questions you might have
How do I use the detect_face tool with Yitu Technology? +
You provide an image URL or upload. The agent returns precise coordinates and attributes for every face it finds in that picture.
Can compare_faces verify two different people? +
No. compare_faces is strictly a one-to-one comparison (1:1). It tells you if the faces match; it cannot tell you how similar they are.
What's the best way to manage my face data with list_repos? +
Use list_repos first to get all repository IDs. Then, use those IDs when running search_face_in_repo to narrow down your search scope.
Does detect_silent_liveness work on old photos? +
Yes. detect_silent_liveness is designed specifically for anti-spoofing, meaning it can spot deepfakes or printed images used to fool the system.
If a person changes their appearance, how do I use `remove_face_from_repo` to update my records? +
You must first call remove_face_from_repo using the old face's unique ID. This clears the outdated biometric template from the repository. After removal, you can re-enroll and add their new facial data using a fresh call to add_face_to_repo.
Does `moderate_image` check for policy violations or inappropriate content before processing? +
Yes, it acts as an initial safety filter. Before your agent processes the image data, moderate_image screens it against predefined policies to flag any prohibited material. This prevents bad data from entering your identity workflow.
What should I do if `search_face_in_repo` returns multiple results with low confidence scores? +
When the returned matches have a low confidence score, you need to manually review them. The tool provides the confidence percentage for each match, so you can filter out uncertain identifications and only act on high-certainty data.
What is the proper workflow when I use `create_face_repo` to set up a new identity archive? +
Create_face_repo establishes the container for your identities, giving you a unique ID. Next, you must populate that repository by calling add_face_to_repo repeatedly with all the initial faces. This completes the basic setup process.
How do I find my Yitu AppID and APIKey? +
Log in to the Yitu Cloud Platform, navigate to the 'Developer Center' or 'API Management' section to find your unique AppID (DevId) and APIKey (DevKey).
What is a facial repository? +
A facial repository is a secure, private database where you store facial features (templates) of known individuals. This allows the system to perform 1:N searches to identify a person from a large group.
How accurate is the identity comparison? +
Yitu is a world leader in facial recognition accuracy. The system returns a confidence score (typically 0.0 to 1.0). A score above 0.8 is generally considered a highly reliable match for the same person.
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