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    • F
      license
      Not graded
      quality
      D
      maintenance
      Enables AI assistants to decode VINs, check stolen vehicle databases, and retrieve market valuations through natural language.
      2
      -
    • A
      license
      A
      quality
      A
      maintenance
      Provides AI agents with instant, structured access to electronic component datasheets, pinouts, and electrical specifications without requiring PDF uploads. It enables seamless part searching, design validation, and side-by-side component comparisons across major hardware providers.
      14
      120 npm
      12
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Provides AI agents with authoritative OEM automotive repair data including part numbers, torque specs, fluid capacities, and service procedures for vehicles 1982-2013 across 80+ makes, sourced from factory service manuals.
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Enables LLMs to decode Vehicle Identification Numbers and look up vehicle makes, models, years, and specifications offline using a curated NHTSA vPIC database.
      8
      1
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Spacecraft component specifications read from vendor datasheets (2,500+ products, 24 categories): search by engineering requirements, compare, budget mass and power, read datasheet digests and facts, find data gaps, and assemble a bus around parts you lock. Every value cites its datasheet page; missing data is named, never guessed.
      51 npm
      1
      MIT
    • A
      license
      A
      quality
      C
      maintenance
      Enables AI assistants to search millions of electronic components and ICs, retrieve specifications, compare alternates, and submit turnkey BOM procurement requests.
      4
      MIT

    TDQS

    A3.5/5.0

    Scored across 14 tools

    Disambiguation4/5

    The tools mostly target clearly distinct operations: hierarchical browsing (groups, subgroups, tables), content retrieval (table parts, part details), search, VIN decoding, supersession history, where-used, and images. The only mild overlaps are get_part/get_part_history/where_used (related but different facets) and get_drawing_image/get_assembly_image (both render exploded views but for different entity types), which descriptions clarify well.

    Naming Consistency4/5

    Almost all tools follow a predictable snake_case verb_noun pattern (list_groups, get_table_parts, search_parts, decode_vin), with a few acceptable noun-centric variants like get_part_history and get_drawing_image. The outlier is where_used, which is a query phrase rather than a verb_noun name, but it remains readable and clear.

    Tool Count5/5

    At 14 tools, the set is well-scoped for a read-only parts catalogue: it covers browsing, search, VIN decoding, part relationships, and imagery without bloat. Each tool has a distinct role and no redundant near-duplicates pad the surface.

    Completeness4/5

    The surface covers the full read-only lifecycle: catalogue navigation, VIN-based fitment, part lookup, supersession history, where-used, modification codes, and exploded views. Minor gaps remain—there is no direct lookup by vehicle attributes (e.g., model/year) without a VIN, and no explicit operation to resolve a table reference number to a part outside get_table_parts—but these are workarounds rather than dead ends.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues