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    • A
      license
      C
      quality
      D
      maintenance
      Provides access to 41 micropayment-based services for blockchain analytics, trading signals, prediction markets, and financial sentiment analysis. It enables users to perform crypto-native tasks like auditing smart contracts, tracking whale alerts, and analyzing DeFi liquidity through natural language.
      41
      35 PyPI
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI agents to access crypto prices, DeFi yields, Polymarket data, Base chain info, and security scans with pay-per-call via USDC on Base mainnet.
      MIT
    • F
      license
      Not graded
      quality
      B
      maintenance
      Enables Claude users to discover creators, browse and buy digital products, tip creators, and manage membership subscriptions across Ethereum, Base, Robinhood, and Solana, with wallet-signature payment approvals and on-chain verification. Supports natural-language creator search, purchase history with download links, and creator-only earnings analytics.
      -
    • A
      license
      A
      quality
      B
      maintenance
      Provides AI agents with a wallet and access to 900+ pay-per-call intelligence APIs across 74 verticals, with automatic USDC payments on Base and built-in spend guardrails.
      5
      36 npm
      MIT

    TDQS

    A3.7/5.0

    Scored across 14 tools

    Disambiguation4/5

    Most tools map to distinct modalities (image, video, code, etc.) with clear boundaries. However, `bittensor_text`, `bittensor_llm`, and `bittensor_reasoning` all handle text generation and could be confused without careful reading of their specific subnet specializations. The `sharpsignal_predict` tool is clearly distinct as the only prediction market service.

    Naming Consistency4/5

    Thirteen tools follow a consistent `bittensor_<capability>` snake_case pattern that clearly indicates their function and source network. The `sharpsignal_predict` tool breaks this convention, though this is semantically justified as it uses Perplexity rather than Bittensor. All tools use descriptive, action-oriented nouns that align with their outputs.

    Tool Count5/5

    Fourteen tools appropriately cover the breadth of Bittensor subnet offerings without excessive bloat. Each tool represents a distinct AI service (text, image, video, code, etc.) that earns its place in a comprehensive generative AI gateway. The count balances granularity with usability.

    Completeness4/5

    The set provides robust coverage of major generative AI modalities including text, image, video, 3D, audio, and code generation, plus data analysis and prediction markets. Minor gaps exist (e.g., no speech-to-text or image editing), but the surface covers the stated purpose of Bittensor subnet access comprehensively. The addition of prediction market intelligence adds valuable orthogonal functionality.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues