ListingGood MCP

Get recommended by Amazon's AI.

CommunityOnlineNo sign-inGlobalFreeRead-only

What it can do

    What data it sees

    Do you need an account

    No: the server works without sign-in

    Get recommended by Amazon's AI. Hosted MCP server for Amazon listing compliance & generation.

    How to connect

    How to connect ListingGood MCP to Claude

    The server address to paste into Claude settings:

    https://listinggood.com/mcp

    1. Open Claude (claude.ai or the desktop app).
    2. Go to Settings → Connectors.
    3. Click Add custom connector.
    4. Paste the server address copied below into Remote MCP server URL and click Add.
    5. In a chat, click + → Connectors and switch the new connector on.

    Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

    Authorization

    After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

    How to connect ListingGood MCP to ChatGPT

    The server address to paste into ChatGPT settings:

    https://listinggood.com/mcp

    1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
    2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
    3. Open Settings → Connectors and click Create.
    4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
    5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
    6. In a new chat open + → Apps/Connectors and enable the connector.

    OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

    Authorization

    On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

    How to connect ListingGood MCP to Cursor

    The server address to paste into Cursor settings:

    https://listinggood.com/mcp

    Fastest: click Open in Cursor below and confirm the prompt.

    Manually:

    1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
    2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
    3. Save the file. The server appears in the MCP list; authorize it there if asked.

    Authorization

    If the service needs sign-in, an authorize button appears next to the server in the MCP list.

    Server tool list (8)

    Raw names from tools/list. Only developers need these.

    ai_readiness_checkScore how likely Amazon's AI (Rufus, COSMO) is to recommend a listing. Free, deterministic, rule-based check on pasted listing copy (title, bullets, description). Returns a compliance health score, an AI-readability score, and a combined AI Recommendation Readiness Score (compliance * 0.55 + readability * 0.45) with actionable suggestions. Use this as a fast baseline BEFORE generating or editing a listing. Do NOT use it for a full compliance report - use compliance_scan for the deep knowledge-base audit. Free, read-only, no API key required, no credits deducted. Args: text: raw listing title + bullets + description (required). marketplace: marketplace code, US/DE/ES/FR/IT/JP/AE/SA/UK (default US). lang: zh or en (default en). email: optional lead email for a confirmation message and lead capture.
    agent_ready_checkScore whether a product listing is Agent-Ready for AI shopping agents (ACP/UCP era). Platform-agnostic: works for Amazon, Shopify, Walmart, TikTok Shop or any storefront that AI shopping assistants may read. Use it when a user asks whether their product will be found, recommended, or auto-purchased by an AI agent. Fully local, deterministic rule engine — no API key, no credits, no network call. Returns four dimensions: structured attributes, entity clarity, trust & compliance, and agent actionability, plus ranked fixes. Args: text: raw product copy — title plus bullets/description (required). platform: amazon | shopify | walmart | tiktok | generic | auto (default auto-detected). lang: en or zh for the report language (default en).
    compliance_checkQuick pre-publish compliance gate before generating a listing. Fast, free scan for obvious red-line words and category risks. Returns a shallow pass/fail-style result, not a full audit. Use this as a cheap pre-check right before generation. Do NOT use it for a complete risk report - use compliance_scan for the deep knowledge-base audit. Read-only; requires an API key; no credits deducted. Args: text: listing copy (required). lang: zh or en (default en). category: optional category hint, e.g. electronics or apparel.
    compliance_scanDeep knowledge-base compliance audit (async; 2 credits). Produces a thorough, written risk report covering prohibited words, IP, category mismatches, GPSR and more, backed by a private 15-year compliance knowledge base. Use this when you need a complete, actionable compliance report. Do NOT use it for a quick pre-publish check - use compliance_check for that. Read-only; deducts 2 credits; runs asynchronously, poll for the result. Args: text: listing title/bullets/description (required). marketplace: marketplace code (default US). category: optional category hint. lang: zh or en (default en). images: optional list of up to 5 data:image base64 strings, each < 4MB.
    generate_poaDraft a submission-ready Plan of Action (POA) from an Amazon violation notice (async; 4 credits). Converts a suspension or removal email into a structured POA appeal. Use this when a listing or account is suppressed. Not free - deducts 4 credits and runs asynchronously, poll for the result. Read-only: it drafts text and does not submit anything to Amazon. Args: text: violation notice or removal email text (required). marketplace: marketplace code (default US). lang: zh or en (default en). violation_type: optional, e.g. ip_complaint / authenticity / policy.
    analyze_reviewAnalyze a negative Amazon review for root cause and a suggested response (async; 2 credits). Extracts the underlying issue from a critical review and drafts a brand-appropriate response angle. Use this after a negative review appears, to decide how to reply. Do NOT use it to generate a listing or an appeal - use generate_listing or generate_poa for those. Read-only; deducts 2 credits; runs asynchronously, poll for the result. Args: text: the negative review text (required). marketplace: marketplace code (default US). lang: zh or en (default en).
    fill_from_sentenceExpand a one-sentence product description into structured listing fields (free; API key; no credits). Turns casual, spoken product copy into structured fields (title, bullets, features) that feed the listing generator. Use this as a low-friction starting point when you only have a rough sentence. Do NOT use it to produce a final optimized listing - use generate_listing for that. Read-only; free; no credits deducted. Args: sentence: one-sentence product description, 4-1000 characters (required). lang: zh or en (default en).
    generate_listingGenerate a high-conversion Amazon listing - title + bullets + description (async; 1 credit per marketplace). Produces A9-optimized copy that respects per-marketplace character limits. Use this to create a full listing from product facts. Not free - deducts 1 credit per selected marketplace and runs asynchronously, poll for the result. Read-only: it drafts text and does not publish. Args: cn_name: product Chinese name (required). sku: product SKU (required). marketplaces: optional list of marketplace codes; defaults to all 9. price: optional product price. lang: zh or en (default en).