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    • A
      license
      Not graded
      quality
      C
      maintenance
      Token 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.
      1
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Pre-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.
      3
      MIT
    • A
      license
      A
      quality
      C
      maintenance
      Enables 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.
      6
      2
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Enables 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.
      6
      MIT
    • A
      license
      A
      quality
      C
      maintenance
      Scans suspicious messages, URLs, and text for scams inside any MCP-compatible AI assistant. No signup or API key needed for anonymous use.
      1
      228 npm
      MIT

    TDQS

    A3.5/5.0

    Scored across 5 tools

    Disambiguation4/5

    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.

    Naming Consistency4/5

    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.

    Tool Count5/5

    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.

    Completeness4/5

    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.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues