Market

Last 30 days

Uptime

100%

Response time

269 ms typical, 269 ms slowest 5%

Last check

6 h ago

Next check

in 19 min

How to call it

Add it to any MCP client that supports remote servers.

{
  "mcpServers": {
    "market-7": {
      "type": "http",
      "url": "https://api.vistoya.com/mcp"
    }
  }
}

6 tools

• discover_products

  Find fashion products using natural language and/or structured filters. Provide a `query` for semantic ranking via multimodal text+image embeddings ("oversized wool coat", "black leather jacket", "minimalist gold jewelry", "linen shirt for a beach wedding under $200") — best for

• find_similar_products

  Given a product ID, find similar products across the entire catalog. Useful for "more like this" recommendations or finding alternatives. Returns compact product cards, not full variant detail; call get_product for SKU-level variants, exact variant prices, merchant description, s

• discover_brands

  Find fashion brands using natural language, structured filters, or both. Best for queries like "Italian streetwear brands", "Scandinavian minimalist brands", "Japanese technical outerwear", "brands with avant-garde tailoring", or qualified similarity such as "brands like Rick Owe

• find_similar_brands

  Given an active catalog brand name or merged alias, find similar brands using brand-profile vectors generated during product indexing. Unknown or ambiguous seeds return no brands. Returns up to 20 brands.

• get_product

  Get the detailed response for a specific product ID. Use this after discover_products or find_similar_products when you need merchant description, store info, all images, SKU-level availability variants, SKU, colorKey/size matrix, exact variant prices/compareAtPrice in the reques

• get_filters

  Returns available filter values in the catalog. By default returns categoryTree plus brands, colors, materials, genders, occasions, seasons, styles, silhouettes, currencies, and price range. Use "fields" to request only specific dimensions — faster and less data. "categoryTree" i

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