AI Catalog by Crescendo Lab
Check if a product feed is ready for AI shopping (ChatGPT, Gemini, AI Mode): score, gaps, fixes.
我該用這個嗎
品質與安全性
根據工具定義與協定合規性的自動化分析。
上下文成本
這是每次將伺服器的工具載入模型上下文時所消耗的約略 token 數量。數量越高，可用於其他工作的注意力就越少。
安裝
一鍵安裝
將以下內容加入你的 `claude_desktop_config.json` 檔案：
{
"mcpServers": {
"ai-catalog": {
"url": "https://catalog.cresclab.com/api/mcp/"
}
}
}遠端端點
https://catalog.cresclab.com/api/mcp/streamable-http它能做什麼
工具清單
工具（3）
🟢scan_product_feed(feed_url, locale)
Fetch a public product feed once and score how well AI shopping assistants can understand it (0-100). Returns the biggest gaps (share of products affected), sample shopper questions AI could not answer from the feed, the thinnest products, and a link to fix the gaps. Accepts Google Merchant / Facebook catalog XML, CSV, TSV, or a Shopify store URL. Takes 20-60 seconds. Only summary results are stored.
輸入結構描述
{
"type": "object",
"properties": {
"feed_url": {
"type": "string",
"description": "Public URL of the product feed or Shopify store, e.g. https://example.com/feed.xml or https://shop.example.com"
},
"locale": {
"type": "string",
"enum": [
"en",
"zh-TW"
],
"description": "Language for shopper questions and labels (default en)."
}
},
"required": [
"feed_url"
]
}🟢get_feed_fix_guide(issue_key)
Explain what a gap from scan_product_feed means, why AI shopping assistants need that data, and how to fix it with evidence the merchant already owns. Call with no argument to get the full checklist.
輸入結構描述
{
"type": "object",
"properties": {
"issue_key": {
"type": "string",
"description": "Issue key from scan_product_feed, e.g. no_specs, thin_desc, llm_unanswerable. Omit for all."
}
}
}🟢list_ai_shopping_platforms
List the main AI shopping surfaces and how product data reaches each one (e.g. Google Merchant Center for Gemini / AI Mode / Shopping; ChatGPT merchant program). Overview only, not a list of live integrations.
輸入結構描述
{
"type": "object",
"properties": {}
}社群
證據