MCP server

Vistoya Market Official

io.github.vistoya/market

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Search and get fashion products recommendations across multiple e-ecom stores

Source

At a glance

Type

MCP server

Publisher

vistoya.com (verified by the MCP Registry)

Status

Working at last check

Tools

6

Token cost

About 4,896 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http market https://api.vistoya.com/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.market]
url = "https://api.vistoya.com/mcp"

Gemini CLI

{"mcpServers":{"market":{"httpUrl":"https://api.vistoya.com/mcp"}}}

Cursor

{"mcpServers":{"market":{"url":"https://api.vistoya.com/mcp"}}}

VS Code

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

Antigravity

agy mcp add market https://api.vistoya.com/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"market","server_url":"https://api.vistoya.com/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"market","server_url":"https://api.vistoya.com/mcp"}

Tools

• discover_brands ~916 tokens
  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 Owens for technical out
• discover_products ~2450 tokens
  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 open-ended discovery
• find_similar_brands ~252 tokens
  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.
• find_similar_products ~412 tokens
  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, store info, and all i
• get_filters ~564 tokens
  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" is a flat DFS-ordered
• get_product ~302 tokens
  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 requested currency, and th

Browse

• GitHub tools
• Website monitoring
• GitHub and code hosting
• Translation
• Searching your documents (RAG)

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