Rug Pull Detection Strategy

Rug Pull Detection Strategy MCP Connector for Claude

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Identify high-risk tokens by analyzing liquidity locks, contract privileges, and holder distribution.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides a deterministic risk assessment engine to protect traders from liquidity removal and developer exit scams. By connecting your AI client to Vinkius Edge, you can use tools like analyze_token_risk to evaluate a token's security profile. The engine calculates a rug risk score based on critical factors: liquidity lock percentage, owner minting privileges, top holder concentration, and transaction limits. It also identifies honeypots and calculates liquidity removal risk. This allows agents to provide clear trading recommendations--Safe, Caution, or Avoid--before capital is committed.

cryptorug-pullrisk-assessmentliquiditytokenomics

3 tools expose this connector's capabilities to your AI agent.

get_holder_metrics

Evaluates the distribution of token ownership to identify centralization risks

analyze_token_risk

Provides a comprehensive risk profile and a final trading recommendation for a specific token

get_liquidity_status

Checks the current state of liquidity to determine if it is sufficient and how much is committed to the protocol

See how to talk to your AI agent using Rug Pull Detection Strategy.

Analyze the risk for token address 0x1234567890abcdef1234567890abcdef12345678 with $150,000 liquidity.

The token has a rug risk score of 25. With 80% liquidity locked and no minting privileges, the recommendation is SAFE.

Check if this token is a honeypot: 0xabc123...

The contract analysis shows the honeypot flag is true, meaning the contract prevents selling. Recommendation: AVOID.

What is the holder concentration for the token at 0xdef456...?

The top 10 holders control 65% of the total supply, indicating high centralization risk.

The score is a weighted sum of penalties: low liquidity locks, owner minting rights, high holder concentration, lack of transaction limits, and low liquidity depth.

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