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Pangolinfo Amazon Data MCP

by Pangolin-spg
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    TDQS

    A4.2/5.0

    Scored across 21 tools

    Disambiguation5/5

    Nearly every tool carries explicit 'Use when' / 'Don't use' guidance that names the sibling tool to prefer instead (e.g. get_amazon_product vs get_amazon_delivery_time vs get_amazon_reviews; list_bestsellers vs list_new_releases vs list_category_products). The scrape_url escape hatch and the four search tools (search_amazon, ai_search, search_amazon_alexa, search_local_maps) are each bounded by source and intent, so an agent can reliably pick one.

    Naming Consistency4/5

    Most names follow a clean verb_noun pattern (search_amazon, get_amazon_product, list_bestsellers, filter_niches, get_category_paths). A few deviate: ai_search and wipo_search invert the order, keyword_trends is noun_noun, and pangolinfo_capabilities uses a vendor prefix, but all remain readable and predictable.

    Tool Count4/5

    21 tools is on the heavy side but justified by the breadth of the platform (Amazon product/review/category/niche data plus Google, Maps, WIPO, and Alexa sources). Each tool covers a distinct surface, though a couple (search_amazon_alexa, get_amazon_alexa_questions) feel like niche additions that push the count near the practical ceiling.

    Completeness4/5

    The surface covers the full data-retrieval lifecycle: keyword search, product/PDP detail, reviews, category tree drilling, category/niche commercial-metric filtering, seller storefronts, and external demand/IP signals, with a generic scrape_url fallback. Account/credits are deliberately excluded and there is no keyword-rank-history tool, but core workflows have no dead ends.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources