MerchantGuard MCP Server

AI-native fraud scoring and risk intelligence for agentic commerce.

A Model Context Protocol (MCP) server that provides real-time fraud detection, risk scoring, and compliance tools for AI agents processing payments. Works alongside any payment MCP server (Worldpay, Stripe, Coinbase, etc.) as the security and trust layer.

Why MerchantGuard MCP?

As AI agents begin transacting autonomously (200M+ agent orders on Alibaba, DoorDash building agentic commerce, Stripe confirming stablecoins as core infrastructure), the payment industry faces a critical gap: existing fraud tools only work within their own rail.

Visa's fraud detection only scores Visa transactions. Stripe Radar only works on Stripe. But agents will use multiple rails simultaneously — cards, stablecoins, crypto, ACH. Who scores across all of them?

MerchantGuard MCP is the cross-rail security layer for the agentic economy.

AI Agent
  -> MerchantGuard MCP (risk scoring)  <-- YOU ARE HERE
    -> Worldpay MCP / Stripe MCP / Coinbase (payment processing)

Tools

Quick Start

Using npm

npm install
npm run build
npm start

Using Docker

docker build -t merchantguard/mcp .
docker run -p 3002:3002 --env-file .env merchantguard/mcp

Using stdio (for Claude Desktop, Cursor, etc.)

npm run start:stdio

Configuration

Copy .env.example to .env:

cp .env.example .env

Integration Examples

With Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "merchantguard": {
      "command": "node",
      "args": ["/path/to/merchantguard-mcp/dist/server-stdio.js"],
      "env": {
        "MERCHANTGUARD_API_KEY": "your_key_here"
      }
    }
  }
}

With Worldpay MCP (Side-by-Side)

An AI agent connects to both servers. Before processing any payment through Worldpay, it first scores the transaction through MerchantGuard:

{
  "mcpServers": {
    "merchantguard": {
      "command": "node",
      "args": ["/path/to/merchantguard-mcp/dist/server-stdio.js"]
    },
    "worldpay": {
      "command": "node",
      "args": ["/path/to/worldpay-mcp/dist/server-stdio.js"]
    }
  }
}

Agent workflow:

1. Call guardscore_transaction_risk to score the transaction
2. If approved, call guardscore_merchant_lookup to verify the merchant
3. If the agent is autonomous, call guardscore_agent_verify to confirm authorization
4. Process payment via Worldpay's take_guest_payment
5. Call guardscore_dispute_predict to assess post-transaction risk

With OpenAI Agentic Commerce Protocol (ACP)

MerchantGuard MCP can serve as the risk assessment layer in ACP flows:

Buyer Agent -> ACP Checkout -> MerchantGuard (risk check) -> Payment Provider -> Merchant

Architecture

src/
  api/
    guardscore.ts          # GuardScore API client
  tools/
    mcp-tool.ts            # Base tool interface
    scoring/
      TransactionRiskScore.ts   # Pre-payment risk scoring
      MerchantLookup.ts         # Merchant intelligence
      AgentVerify.ts            # AI agent verification
    monitoring/
      DisputePredict.ts         # Chargeback prediction
      VelocityCheck.ts          # Velocity anomaly detection
    compliance/
      CrossRailCheck.ts         # Cross-rail fraud detection
      VAMPAnalysis.ts           # Visa VAMP compliance
  schemas/
    schemas.ts             # Zod validation schemas
  types/
    index.ts               # TypeScript type definitions
  utils/
    logger.ts              # Logging
    mcp-response.ts        # MCP response helpers
  merchantguard-mcp-server.ts  # Main server class
  server-http.ts               # HTTP transport entry point
  server-stdio.ts              # stdio transport entry point

About MerchantGuard

MerchantGuard is the privacy-preserving fraud scoring platform for high-risk and agentic commerce. Our patented cross-rail fraud detection technology scores transactions across card networks, stablecoins, and crypto — providing unified risk intelligence that rail-specific tools can't match.

Patent Portfolio: Cross-Lingual BFT Paradox scoring, Multi-Agent AI Security, and GuardScore risk engine.

Member: Agentic AI Foundation (AAIF) by the Linux Foundation.

Privacy-Preserving Architecture: OPRF behavioral fingerprinting (RFC 9497), soulbound agent credentials, zero-knowledge trust verification. Agents prove compliance without exposing internals.

License

MIT