rugradar
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- FlicenseBqualityCmaintenanceEnables AI agents to perform token security audits, honeypot detection, liquidity analysis, and risk scoring to avoid crypto scams.1-
- AlicenseNot gradedqualityCmaintenanceToken safety oracle for AI agents. Honeypot detection, 17 scam pattern checks, LP lock verification across 6 EVM chains. Score 0-100 with risk flags. ERC Token Safety Score standard.1MIT
- AlicenseAqualityBmaintenancePre-trade token safety check for AI agents. Simulates a sell before you buy, then reports honeypot, tax, liquidity, pair age, same-ticker impersonation and owner powers as one low/medium/high/unknown verdict. Fail-closed: when a critical check cannot run it answers unknown rather than guessing low. Publishes its own measured error rate with the benchmark harness in the repo.3MIT
- AlicenseAqualityCmaintenanceEnables AI agents to scan crypto tokens for rug pulls, scams, and risk using a six-agent consensus system. It provides real-time security audits and risk scoring for tokens on Solana, Ethereum, Base, and BSC.62MIT
- AlicenseAqualityBmaintenanceEnables AI assistants to assess the risk of crypto wallets, tokens, and smart contracts by providing read-only on-chain analysis, 0-100 risk scoring with explanations, fund tracing, and contract inspection before interaction.6MIT
- AlicenseAqualityCmaintenanceScans suspicious messages, URLs, and text for scams inside any MCP-compatible AI assistant. No signup or API key needed for anonymous use.1228 npmMIT
TDQS
Scored across 5 tools
Tools have distinct purposes: check_token and scan_message both assess scams but differ in scope (network vs. text-only), and get_report vs. token_history retrieve different historical data. However, check_token's inclusion of message red flags creates minor overlap with scan_message, and both historical tools could be confused.
Four tools follow a verb_noun pattern (get_report, check_token, scan_message, explain_finding), but token_history uses a noun_noun pattern, a minor deviation. Overall consistent and readable.
Five tools are well-suited for a focused token scam checker; each covers a distinct need (checking, scanning, retrieving, explaining). No tools feel redundant or missing.
The surface covers token checking, message scanning, report retrieval, history, and finding explanations, which is comprehensive for the domain. Minor gap: no tool to list or search saved reports, but agents can work around it.