Stop Getting Rugged: Real-Time Solana Token Risk Analysis for Your AI Agents

You're building with Solana meme tokens, but manually vetting each one for rug pull risks is killing your productivity. The rug-check-mcp server gives your AI agents instant access to comprehensive token risk analysis, so you can make informed decisions without the research overhead.

The Problem with Meme Token Due Diligence

Manual token analysis is a time sink. You're checking liquidity locks, examining holder distributions, verifying mint authorities, and cross-referencing multiple data sources just to determine if a token is safe to touch. Meanwhile, opportunities slip by and your development velocity suffers.

Most developers either skip due diligence entirely (risky) or build custom analysis scripts (time-consuming). Neither approach scales when you're working with AI agents that need to evaluate tokens programmatically.

Key Benefits

Instant Risk Assessment: Get structured risk scores (0-100) with detailed breakdowns of high/moderate/low risk factors in seconds, not hours.

AI-Agent Ready: Clean JSON responses with specific risk categories that your agents can understand and act upon. No parsing messy HTML or juggling multiple APIs.

Comprehensive Analysis: Beyond basic checks, you get holder concentration analysis, liquidity provider counts, mint/freeze authority status, and LP burn verification all in one call.

Real-World Risk Categories: The server doesn't just give you numbers - it explains what each risk means with boolean flags for specific concerns like "Tokens auto-freeze risks found" or "Significant ownership by top 10 wallets."

Use Cases That Actually Matter

DeFi Portfolio Management: Your AI agent can automatically screen potential investments, rejecting tokens with high rug pull indicators before they enter your portfolio.

Trading Bot Risk Guards: Build safety checks into your trading algorithms. Instead of hardcoding token allowlists, let your bots dynamically assess new opportunities while avoiding obvious traps.

Research Pipeline Automation: Screen hundreds of meme tokens programmatically, filtering down to the subset worth deeper analysis. The structured output makes it easy to sort by risk score or specific criteria.

Client Advisory Tools: If you're building tools for other crypto users, integrate real-time risk analysis directly into your interfaces. Show users why a token might be risky before they invest.

Here's what you get back for token 9VxExA1iRPbuLLdSJ2rB3nyBxsyLReT4aqzZBMaBaY1p:

{
  "snif_score": 55,
  "market_cap": 271893.94,
  "risks": {
    "high": {
      "count": 3,
      "details": {
        "Mintable risks found": true,
        "Freeze risks found": true,
        "Permanent control risks found": true
      }
    }
  },
  "audit_risk": {
    "mint_disabled": true,
    "freeze_disabled": true,
    "lp_burned": false
  }
}

Your agent immediately knows this token has concerning control mechanisms despite some positive audit signals.

Integration

Add the server to your Claude Desktop configuration:

{
  "mcpServers": {
    "rug-check-mcp": {
      "command": "python",
      "args": ["path/to/rug-check-mcp/main.py"],
      "env": {
        "SOLSNIFFER_API_KEY": "your_api_key"
      }
    }
  }
}

Your AI agents can now call analysis_token with any Solana token address and get structured risk data back. The server handles API rate limiting and data formatting, so your agents get consistent, actionable information.

For programmatic access, the MCP protocol means you can integrate this into any workflow that supports MCP servers - not just Claude Desktop. Build it into your own trading tools, portfolio managers, or research platforms.

The server requires a Solsniffer API key (free tier available), handles all the complex data parsing, and returns results in a format designed for automated decision-making. No more custom scraping scripts or manual token research - just clean, reliable risk analysis at MCP speeds.