Rugcheck Ai

io.github.MrWizardlyLoaf由 GitHub 账号 MrWizardlyLoaf 发布v1.2.0更新于 Oct 24, 2026

Solana token safety for AI agents — rug-pull, honeypot & Token-2022 trap detection before you buy.

已验证Streamable HTTP可网页运行金融安全与监控

概览

AI 生成的概览

远程 MCP 服务器,用于筛查 Solana 代币的跑路、蜜罐与 Token-2022 陷阱,并生成未签名的 Jupiter 买入交易。

可以做什么
  • 扫描 Solana 代币,给出 SAFE/CAUTION/DANGER 结论与 0-100 安全评分,或快速的是/否判断
  • 检查铸造与冻结权限、Token-2022 扩展以及掌控代币的钱包
  • 测试可卖出性与往返成本,并查看流动性、成交量、代币年龄和持仓集中度
  • 估算跑路概率与紧迫窗口、评估诈骗意图评分,并比较或批量扫描最多 10 个代币
  • 为通过筛查的代币构建带 Jito 小费的未签名 Jupiter 兑换交易
适用场景
当智能体在 Solana 上交易或评估 SPL 与 Token-2022 代币,需要在花钱前快速做链上尽职调查时使用,包括尚未被其他平台收录的新发代币。
运行要求
远程 Streamable HTTP 端点;托管服务无需安装或 API 密钥。自托管需安装 Python 依赖,并通过 SOLANA_RPC 环境变量提供 Solana RPC 地址。部分客户端需借助 mcp-remote 桥接连接。
安装前请注意
筛查为只读,但 execute_safe_swap 会构建真实的兑换交易,签名后将动用资金并包含 Jito 小费;服务器声明不持有私钥、不签名。结论与跑路预测均为启发式判断,并非保证,链上状态会实时变化。查询会发送至第三方托管端点。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Rugcheck Ai,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.MrWizardlyLoaf%2Frugcheck-ai/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "rugcheck-ai": {
      "type": "http",
      "url": "https://web-production-58d585.up.railway.app/mcp"
    }
  }
}

README

🛡️ RugCheck AI — On-chain Token Safety for Solana AI Agents

[version] [license] [MCP] [python] [transport] [CI]

A lightweight MCP server that reads a Solana token's mint directly from the chain to screen for the common rug & honeypot traps before your agent trades — active mint/freeze authority and dangerous Token-2022 extensions (permanent delegate, transfer hooks, non-transferable, pausable) — then, for tokens that pass, builds the buy as an unsigned Jupiter transaction carrying a small Jito tip for faster inclusion. You sign it; the server never holds keys.

Quickstart (30 seconds)

No install, no API key. Point your agent at the remote endpoint:

https://web-production-58d585.up.railway.app/mcp

Then ask one question before any buy:

scan_token("DezXAZ8z7PnrnRJjz3wXBoRgixCa6xjnB7YaB1pPB263")   # BONK→ { verdict: "SAFE", safety_score: 100, sellable: true, risks: ["no red flags found"] }

SAFE → clear. CAUTION → read the risks. DANGER → don't buy. That's the whole loop.

See it catch a rug

Three real mainnet tokens, one scan_token call each (scanned 2026-06-17 — re-run to verify, live state changes):

TokenVerdictWhy
BONKSAFE · 100authority renounced, liquid, sellable — no red flags
USDCCAUTION · 70issuer keeps an active mint and freeze authority — your balance can be frozen
fresh pump token 24tF…i9pumpDANGER · 20no sell route (honeypot), 100% held by one wallet, $0 liquidity

The honeypot is the one that matters: you could buy it and never sell. RugCheck AI flags it before your agent spends a cent — even though the token is too new to be indexed anywhere else. Even USDC comes back CAUTION, not SAFE, because the issuer can still freeze your balance — the verdict tells you the truth, not a marketing label.

Tools

Screening

  • scan_token — full safety report in one call: authority, Token-2022 traps, honeypot, liquidity & holder concentration → SAFE/CAUTION/DANGER + a 0–100 score
  • is_safe — quick yes/no gate: one boolean before you trade
  • verify_token_safety — on-chain audit: mint/freeze authority + Token-2022 traps + live market
  • check_authorities — mint / freeze authority and extension detection
  • simulate_sell — can the token actually be sold? (honeypot check)
  • simulate_trade — full round-trip (buy then sell back): real entry/exit cost & round-trip loss %
  • check_liquidity — DEX liquidity, 24h volume, age, buys/sells
  • holders_breakdown — top-holder concentration (dump risk)
  • token_age — freshness + real trading activity
  • rug_forecast — heuristic rug ETA: probability + urgency window + factors
  • scammer_dna — intent score (0–100): how much the token's structure looks like a deliberate scam
  • check_deployer — the wallets that hold power over the token
  • compare_tokens — rank a basket of tokens safest-first
  • batch_scan — scan up to 10 tokens at once, one report each

Execution

  • execute_safe_swap — re-screens the mint, then builds a Jito-tipped Jupiter swap (unsigned); refuses tokens that scan DANGER

Connect

Remote (Streamable HTTP) — no install, point your agent at:

https://web-production-58d585.up.railway.app/mcp

Listed on the official MCP Registry as io.github.MrWizardlyLoaf/rugcheck-ai.

Self-host:

bash
pip install -r requirements.txtSOLANA_RPC=<your-rpc-url> python server.py

Add it to your agent

Cline / Claude Dev (VS Code) — in cline_mcp_settings.json:

json
{ "mcpServers": { "rugcheck-ai": { "url": "https://web-production-58d585.up.railway.app/mcp" } } }

Claude Desktop — in claude_desktop_config.json:

json
{ "mcpServers": { "rugcheck-ai": { "command": "npx", "args": ["-y", "mcp-remote", "https://web-production-58d585.up.railway.app/mcp"] } } }

Cursor — Settings → MCP → Add → Streamable HTTP, then paste the endpoint URL.

Any MCP client — it's a standard Streamable HTTP MCP server; point your client at the /mcp endpoint and the 15 tools appear.

Why

Most agents trade Solana tokens blind. RugCheck AI calls getAccountInfo on the mint and reads the authorities and Token-2022 extensions itself, so you get a real verdict on a fresh launch instead of unknown — and a live mint or freeze authority is flagged before you buy, not after.

Use it when

Your agent needs to answer, before it spends a cent:

  • Is this Solana token safe to buy — or is it a rug pull?
  • Is this a honeypot — will I actually be able to sell after I buy?
  • Does the mint have an active freeze / mint authority that can trap or dilute me?
  • Is there a hidden Token-2022 trap (permanent delegate, transfer hook) that can drain me?
  • Pre-trade screening / token due-diligence for an autonomous trading agent.

Built for AI trading agents, snipers and bots that buy SPL / Token-2022 tokens and need a fast on-chain rug check before entering — then a screened Jupiter route once a token clears.

FAQ

How do I check if a Solana token is safe to buy? Call scan_token(mint) — one call returns a SAFE / CAUTION / DANGER verdict covering mint/freeze authority, Token-2022 traps, honeypot (sellability), liquidity and holder concentration, plus a 0–100 safety score.

How do I detect a honeypot before buying? simulate_sell(mint) checks whether a live sell route exists — a token with no route is effectively a honeypot even when nothing on-chain formally blocks selling.

How do I check holder concentration / whale dump risk? holders_breakdown(mint) reports the largest wallets and what share of supply they control — high concentration means one holder can crash the price on you.

How do I know if a token is a rug pull? rug_forecast(mint) gives a heuristic rug probability and urgency window from real signals (authority, Token-2022 traps, concentration, sell pressure, age). check_authorities and check_deployer show exactly who holds power over the token.

Does it work on fresh / newly launched tokens? Yes — it reads the mint directly on-chain (getAccountInfo), so you get a real verdict on a token too new to be indexed elsewhere. token_age shows freshness and real trading activity.

Does it touch my wallet or sign anything? No. Screening is read-only; execute_safe_swap only builds an UNSIGNED transaction for you to sign — the server never holds keys, never signs, never sends.

Is it free? Do I need an API key? Remote server, no install, no API key. Point your agent at the endpoint and call the tools.

Status

v1.2.0 — working, actively developed, CI-tested. Open source, auditable — the screening tools are read-only (getAccountInfo); execute_safe_swap only builds an unsigned transaction for you to sign.


MIT licensed. Self-hostable. Built for Solana trading agents.

来源:README.md,提交 58266dc

工具

0
SourceWeft 尚未读取此服务器的工具。

版本历史

1
  1. v1.2.0最新Sep 16, 2026

评分

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