FaceSign

Instead of manually wiring nodes and writing integration code, you describe what you need — the MCP builds the verification flow, previews it locally, and exports a production-ready application.

By the end of this section, you'll have a working FaceSign verification session running from your AI assistant.

Add the FaceSign server to your client's MCP configuration:

{ "mcpServers": { "facesign": { "type": "http", "url": "https://mcp.facesign.ai/mcp" } } }

Once connected, the MCP needs your API key to authenticate with FaceSign:

Tell the MCP: "Set my FaceSign API key to sk_test_your_key_here"

The MCP callsset_api_key, loads the available avatars and languages, and confirms the connection.

Tell your AI assistant what verification flow you need. Be specific about the use case:

"Build a step-up verification for wire transfers. It should check liveness, ask a security question about the transaction, and send an email OTP."

- Ask you clarifying questions (avatar choice, failure handling, etc.)
- Assemble the verification flow from your answers
- Validate the flow structure

Once the flow is built, the MCP opens a browser preview:

"Launch a preview so I can test this flow"

This callslaunch_session_ui, which opens a local web page. ClickStart Sessionto run through the verification with your camera and microphone.

When the flow looks right, export it as a deployable Next.js application:

"Export this as a Next.js app"

This callsexport_app, which generates a complete project — React frontend, API routes for session creation,.env.localtemplate, and a README with deployment instructions. SetFACESIGN_API_KEYin your environment and deploy to any Node.js host (Vercel, Railway, Fly).

Session creation goes through the@facesignai/apipackage, which is server-side only — it uses yourFACESIGN_API_KEY, which must never be exposed to the browser. Next.js gives us a runtime where the key stays on the server (API routes / server actions) while the verification UI runs in the client. That's why the export target is Next.js, not a static bundle.

FaceSign MCP works with any client that supports the Model Context Protocol. Pick yours:

Add FaceSign to your project config. Claude Code has full tool support with interactive prompts.

{ "mcpServers": { "facesign": { "type": "http", "url": "https://mcp.facesign.ai/mcp" } } }

Or add it globally at~/.claude.jsonto make it available in all projects.

Add FaceSign to your Claude Desktop configuration.

{ "mcpServers": { "facesign": { "type": "http", "url": "https://mcp.facesign.ai/mcp" } } }

- macOS:~/Library/Application Support/Claude/claude_desktop_config.json
- Windows:%APPDATA%\Claude\claude_desktop_config.json

Add FaceSign to your Cursor MCP settings.

{ "mcpServers": { "facesign": { "type": "http", "url": "https://mcp.facesign.ai/mcp" } } }

Any MCP client that supports Streamable HTTP transport can connect to FaceSign at:

The server is unauthenticated at the transport layer — the first tool call should beset_api_keyto initialize the session with your FaceSign API key.

The FaceSign MCP server exposes five tools for building and managing verification sessions:

Authenticates the MCP server with your FaceSign API key. Must be called before any other tool.sk_test_keys connect to the sandbox,sk_prod_keys connect to production. On success the server loads the available avatars and languages.

"Set my FaceSign API key to sk_test_abc123"

Opens a local web page where you can run through a verification session interactively. A new session is created each time you clickStart Session, so page refreshes work correctly.

"Launch a preview with a liveness check followed by a security question"

Flows must be linear with no loops or cycles. A node must never navigate back to a previous node. All paths move forward toward an END node.

Generates a standalone Next.js application from the current flow, ready for deployment. The exported project includes the frontend, API routes for session creation, an.env.localtemplate, and a README. SetFACESIGN_API_KEYin your environment and deploy to any Node.js host.

"Export this flow as a Next.js app"

Retrieves the full results of a verification session — transcript, AI analysis, node reports, and media references.

"Show me the results of session <id>"

Queries past verification sessions with optional filtering by status, date range, client reference, or free-text search. Returns a cursor-paginated list.

"Show me the last 10 completed sessions"

FaceSign flows are assembled from these 13 node types:

Each recipe shows a natural-language prompt you give your AI assistant and what the MCP does behind the scenes. Pick one to see the full pattern.

Wire transfer with coercion detection.The most common FaceSign use case: verify the account holder before a high-value transfer, and detect if they are acting under duress.

Build a step-up verification for wire transfers over $5,000. Start with liveness detection, then ask the user to confirm the transfer details in a conversation. Include coercion detection. Send an email OTP as the final step.

START— Initializes the verification session.

LIVENESS_DETECTION— Interactive deepfake check. The avatar asks the user to perform randomized actions to confirm a live human.

CONVERSATION— The avatar asks the user to confirm the recipient, amount, and purpose. Six AI models run in parallel: coercion detection analyzes vocal stress, gaze patterns, and response timing throughout.

TWO_FACTOR_EMAIL— Sends a one-time passcode to the user's email for a second authentication factor.

END— Session completes. Results are available via webhook orget_session.

Coercion detection runs across every node, not as a separate step. The conversational node gives it the richest signal because the user is speaking freely.

Account recovery without passwords.Replace security questions with a face-based recovery flow. The user proves identity through biometric recognition instead of knowledge-based answers.

Build an account recovery flow. Collect the user's email, verify their face against our stored biometric, then do a liveness check. If everything passes, let them proceed to password reset.

ENTER_EMAIL— Collects the user's email address to look up their account.

RECOGNITION— Compares the live face against the stored biometric fingerprint for that account.

LIVENESS_DETECTION— Confirms the person is physically present (not a photo or deepfake).

END— Session completes. Your app receives the verification result and can proceed with the password reset.

KYC-style onboarding with document scan.While FaceSign is designed for post-login step-up, you can build a first-time identity enrollment flow that captures a document and creates a biometric fingerprint for future recognition.

Build an onboarding flow for new users. Capture their government ID, scan their face, do a liveness check, then have the avatar welcome them and explain how future verifications will work.

DOCUMENT_SCAN— Captures and validates the user's government-issued ID.

FACE_SCAN— Captures the user's face and creates a biometric fingerprint for future recognition.

LIVENESS_DETECTION— Confirms a live human is present.

CONVERSATION— The avatar welcomes the user and explains what to expect in future verification sessions.

END— Session completes. The biometric fingerprint is stored for futureRECOGNITIONnodes.

Export and deploy to production.After building and testing any flow, export it as a production-ready application.

Export this flow as a Next.js app. I want to deploy it to Vercel.

- Callsexport_appwith the current flow.
- Generates a complete Next.js project containing the React frontend component with the FaceSign widget, API routes for creating and managing sessions, an.env.localtemplate, and deployment-ready configuration.

cd exported-app npm install echo "FACESIGN_API_KEY=sk_prod_your_key" > .env.local vercel deploy

If something isn't working with the FaceSign MCP, start here. Each section covers a specific failure mode with symptoms and fixes.