GoPlus Security
MCP ServerFreeAssess web3 threats by analyzing tokens, NFTs, and wallet addresses. Detect potential rug pulls, flag known phishing sites, and evaluate address reputation across supported chains. Leverage built-in docs and chain coverage to streamline due diligence.
Capabilities4 decomposed
web3 threat analysis for tokens and nfts
Medium confidenceThis capability assesses potential threats in the web3 ecosystem by analyzing tokens and NFTs using a combination of on-chain data and heuristics. It employs a multi-chain architecture to evaluate the reputation of addresses and flag suspicious activities, leveraging a comprehensive database of known phishing sites and rug pulls. The integration of built-in documentation allows users to streamline their due diligence process effectively.
Utilizes a multi-chain architecture that aggregates data from various blockchains to provide a comprehensive threat assessment, unlike many tools that focus on a single chain.
More comprehensive than single-chain tools as it evaluates risks across multiple blockchain environments.
address reputation evaluation
Medium confidenceThis capability evaluates the reputation of wallet addresses by analyzing historical transaction data and interactions with known entities. It uses a scoring system based on various factors such as transaction patterns, associations with flagged addresses, and community feedback. This approach allows for a nuanced understanding of an address's trustworthiness in the web3 space.
Incorporates community feedback into the reputation scoring system, providing a more dynamic assessment compared to static databases.
Offers a more holistic view of address trustworthiness by integrating community insights, unlike traditional methods that rely solely on transaction history.
phishing site detection
Medium confidenceThis capability detects known phishing sites by cross-referencing URLs against a continuously updated database of flagged domains. It employs a combination of pattern recognition and heuristic analysis to identify potential threats, alerting users to high-risk sites before they engage. The system is designed to provide real-time alerts as users interact with web3 applications.
Utilizes a continuously updated database and real-time analysis to detect phishing threats, ensuring users are protected as they navigate web3 environments.
More proactive than traditional methods that rely on user reports or static lists, providing real-time protection.
rug pull detection
Medium confidenceThis capability identifies potential rug pulls by analyzing token liquidity, transaction volume, and developer activity. It employs machine learning algorithms to detect unusual patterns that may indicate a rug pull, such as sudden drops in liquidity or high transaction volumes followed by rapid sell-offs. This predictive analysis helps users avoid investments in high-risk projects.
Employs machine learning to analyze complex market behaviors, providing a more sophisticated detection method compared to rule-based systems.
More accurate than traditional heuristic methods by leveraging predictive analytics to identify potential scams.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Rug Munch Intelligence — MCP Server
# Rug Munch Intelligence — MCP Server [](https://modelcontextprotocol.io) [](https://cryptorugmunch.app/api/agent/v1/status) [](https://
Best For
- ✓developers building security tools for web3 projects
- ✓investors conducting due diligence on crypto assets
- ✓security analysts monitoring wallet activities
- ✓developers creating tools for wallet verification
- ✓web3 users looking for security tools
- ✓developers building anti-phishing solutions
- ✓investors looking to avoid scams
- ✓developers creating risk assessment tools
Known Limitations
- ⚠Limited to supported chains; may not cover all tokens or NFTs
- ⚠Analysis accuracy depends on the completeness of the threat database
- ⚠Reputation scores may not reflect real-time changes
- ⚠Dependent on the availability of historical data
- ⚠Detection accuracy is dependent on the database's currency
- ⚠May not catch newly created phishing sites immediately
Requirements
Input / Output
UnfragileRank
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Repository Details
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Assess web3 threats by analyzing tokens, NFTs, and wallet addresses. Detect potential rug pulls, flag known phishing sites, and evaluate address reputation across supported chains. Leverage built-in docs and chain coverage to streamline due diligence.
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