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Xrpl Risk Scorer

@kynto2001-ctrl

Xrpl Risk Scorer について

Check any XRPL wallet for risk before sending XRP.

基本情報

カテゴリ

その他

トランスポート

stdio

公開者

kynto2001-ctrl

投稿者

Kynto

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "xrpl-risk-scorer": {
      "command": "node",
      "args": [
        "mcp-server.mjs"
      ],
      "description": "XRPL wallet risk scoring with 21 signals, OFAC sanctions screening, and x402 payments"
    }
  }
}

ツール

3

Full 21-signal analysis

Fast 3-second pre-payment

Plain English explanation

概要

What is Xrpl Risk Scorer?

Xrpl Risk Scorer is an MCP server that checks any XRPL wallet address for risk before sending XRP. It provides a full 21‑signal analysis, a fast 3‑second pre‑payment check, and plain‑English explanations of risk scores.

How to use Xrpl Risk Scorer?

Clone the repository (git clone https://github.com/kynto2001-ctrl/wallet-risk-service), install dependencies (npm install), and run the MCP server (node mcp-server.mjs). Then use prompts such as “Check if rHb9CJAWyB4rj91VRWn96DkukG4bwdtyTh is safe to send XRP to” to invoke the tools.

Key features of Xrpl Risk Scorer?

  • check_xrpl_wallet_risk – full 21‑signal analysis including sanctions and scam databases
  • quick_xrpl_prescore – 3‑second verdict: ALLOW, CHALLENGE, or BLOCK
  • explain_xrpl_risk_score – plain‑English explanation with actionable recommendations
  • Detects wash trading, bot timing, and fan‑out patterns
  • Integrates known scam database for additional safety
  • Live demo available at xrplriskscore.ai

Use cases of Xrpl Risk Scorer

  • Check an XRPL wallet’s safety before sending XRP
  • Perform a fast pre‑payment risk screen with a three‑second verdict
  • Understand a risk score result and receive actionable recommendations
  • Integrate risk scoring into an automated XRP transaction pipeline

FAQ from Xrpl Risk Scorer

What does the full risk analysis include?

The check_xrpl_wallet_risk tool evaluates 21 signals, including sanctions screening, wash trading detection, bot timing analysis, fan‑out pattern detection, and a known scam database lookup

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