AI Storefront Readiness Grader
Grades any storefront URL A-F on 20 weighted conversion checks. $1.00/call via x402.
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": {
"storefront-grader": {
"url": "https://grader-al6bxcngia-ue.a.run.app/mcp"
}
}
}遠端端點
https://grader-al6bxcngia-ue.a.run.app/mcpstreamable-http它能做什麼
工具清單
工具（1）
🟢grade_storefront(url)
Audit an ecommerce storefront for AI-agent shopping readiness. Fetches the store server-side and runs 20 weighted checks across product intelligence (schema.org Product/Offer price, availability, variants, GTIN/MPN), AI discovery (robots.txt, llms.txt, agents.md, sitemap.xml, UCP manifest, MCP endpoint), conversation readiness (contact info, FAQ/returns, site search), transaction readiness (HTTPS, checkout signals, tax/shipping as values), and trust (business identity, policies). Returns a machine-readable JSON report: 0-100 score, A-F grade, per-check pass/partial/fail/unknown with evidence, and the top 3 prioritized fixes. Cost: $1.00 USDC on Base per call, paid via x402 — the caller's agent pays from its owner's approved wallet; no API key needed. Use this to decide whether a store is ready for AI shopping agents, or to generate the audit behind a readiness report for a merchant.
輸入結構描述
{
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "Storefront URL to grade, e.g. https://example-store.com"
}
},
"required": [
"url"
]
}社群
證據