Build with AI

FaceSign exposes a Model Context Protocol (MCP) server that lets AI assistants create, test, and export verification sessions through natural language.

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.

What is FaceSign?

Why MCP for step-up authentication?

Quickstart

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

Prerequisites

You need two things:

1. An API key.

If you don't have a key yet, email developers@facesign.ai with your company name and intended use case. Sandbox keys are free.

2. An MCP-compatible client.

Claude Code, Claude Desktop, Cursor, or any client that supports the Model Context Protocol over Streamable HTTP.

Step 1: Add FaceSign to your MCP config

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

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

Step 2: Set your API key

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 calls set_api_key, loads the available avatars and languages, and confirms the connection.

Step 3: Describe what you want to build

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."

The MCP will:

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

Step 4: Preview locally

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

"Launch a preview so I can test this flow"

This calls launch_session_ui, which opens a local web page. Click Start Session to run through the verification with your camera and microphone.

Step 5: Export for production

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

"Export this as a Next.js app"

This calls export_app, which generates a complete project — React frontend, API routes for session creation, .env.local template, and a README with deployment instructions. Set FACESIGN_API_KEY in your environment and deploy to any Node.js host (Vercel, Railway, Fly).

Supported clients

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

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

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

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

https://mcp.facesign.ai/mcp

Tools

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

set_api_key

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"

launch_session_ui

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

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

export_app

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.local template, and a README. Set FACESIGN_API_KEY in your environment and deploy to any Node.js host.

"Export this flow as a Next.js app"

get_session

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

"Show me the results of session <id>"

list_sessions

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"

Node types available in flows

FaceSign flows are assembled from these 13 node types:

Recipes

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.

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.

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.

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.

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

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

Troubleshooting

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

Still stuck?

Contact developers@facesign.ai with your client name and version, the error message or unexpected behavior, and your MCP config (with the API key redacted).

When to use MCP vs. the API

Both paths produce the same result — a verification session powered by the same API. MCP is faster for getting started; the REST API gives you full control.

What's next?