# Judge.me MCP > Judge.me connects your AI agent directly to a product review platform, giving it instant access to customer feedback data. Use this MCP to retrieve specific product details, list all reviews and media attachments, track customer questions, analyze answers, and check for active coupons. It’s the fastest way to automate social proof management and deep-dive into e-commerce sentiment. ## Overview - **Category:** ecommerce - **Price:** Free - **Tags:** product-reviews, social-proof, customer-feedback, ratings, ecommerce-marketing ## Description If you're spending time manually pulling metrics from Judge.me, this MCP changes that. Your AI agent can instantly access your entire product review history, allowing you to analyze customer sentiment without jumping between tabs or exporting CSVs. You can pull full details for individual reviews, figure out which products need attention by listing all available items, and even audit the status of every question asked in your shop’s Q&A section. It's more than just reading data; it lets you run comprehensive analyses on customer feedback, tracking everything from initial product listings to coupon usage. By connecting this MCP via Vinkius, your agent gets a single source of truth for all things customer-facing and review related. ## Tools ### get_product Retrieves core details about a specific product, giving you context on its general performance in the shop. ### get_question Pulls detailed information for a single customer question so you can prepare an accurate official response. ### get_review Fetches all metadata and moderation status for one specific review, ideal when analyzing a particular case. ### get_settings Retrieves the shop's core system settings within Judge.me, useful for auditing widget or email configurations. ### list_answers Provides an overview of all answers written to questions, allowing you to audit if customer queries have been resolved. ### list_coupons Lists every active discount coupon available in your shop, which is helpful for reviewing incentive programs. ### list_medias Retrieves all images or videos attached to product reviews, essential for analyzing user-generated content. ### list_products Provides a list of every item in your shop, along with product names and aggregate review counts for quick analysis. ### list_questions Lists all customer questions asked about products, including their status and unique IDs for tracking.

### list_reviews Gathers a complete list of every product review, showing names, ratings, titles, and bodies to monitor overall sentiment. ## Prompt Examples **Prompt:** ``` List all recent product reviews in Judge.me. ``` **Response:** ``` I'll fetch the latest reviews from your Judge.me account. ``` **Prompt:** ``` Show me the questions asked for product ID '123'. ``` **Response:** ``` I'll retrieve the customer questions associated with that specific product. ``` **Prompt:** ``` Check for any active discount coupons. ``` **Response:** ``` I'll look up the list of active coupons in your Judge.me settings. ``` ## Capabilities ### Analyze Product Performance Get specific details on any listed product, allowing you to gauge its general performance from the outset. ### Review Customer Feedback Retrieve deep-dive information for a single review, including metadata and moderation status, crucial for detailed case analysis. ### Audit Question & Answer Cycles List all customer questions or retrieve specific answers to check if inquiries have been resolved by your team. ### Track Promotions and Settings Pull lists of active discount coupons or view the shop's core settings for system auditing. ### Gather Content Assets List all media, like images and videos, attached to reviews for analyzing user-generated content. ## Use Cases ### Identifying a Product Crisis A product manager needs to know if a specific item is suddenly getting bad reviews. They ask their agent to list all products, then filter for the problematic ID. Finally, they use get_review on the latest comments to summarize common complaints like 'battery life' or 'sizing issues'. ### Tackling Unanswered Customer Questions A content team member notices customer questions piling up. They ask their agent to run list_questions, which shows ten open inquiries. The agent then uses get_question on the top three to pull all necessary context before drafting official answers. ### Reviewing Campaign Effectiveness The marketing team wants to know if their 'leave a review and get 10% off' campaign worked. They ask the agent to list_coupons, check for coupon usage patterns, and cross-reference it with recent reviews using list_reviews. ### Building Competitor Feature Lists A product team needs feature parity data. The analyst asks the agent to use get_settings to pull core shop configurations, seeing what widgets or features are available for them to implement on their own site. ## Benefits - Go beyond simple rating counts. By calling get_review, you pull deep-dive information and metadata for individual feedback, letting your agent analyze *why* a review was written, not just that it was. - Stop tracking product health manually. Using list_products gives your AI client an immediate roster of all items with their current aggregate review counts, guiding where the analysis needs to focus. - Audit support effectiveness instantly. Instead of checking the admin panel, calling list_questions and then list_answers lets your agent see a full picture: which questions are unanswered and who responded. - Automate social proof management. Your agent can pull all available media through list_medias, making it easy to gather and categorize user-generated content for marketing copy or site improvements. - Monitor incentives with precision. The ability to call list_coupons means you don't have to guess which rewards are active; your agent confirms the exact discount codes in use. ## How It Works The bottom line is that instead of writing a series of manual API calls or clicking through multiple admin pages, you ask your agent one question, and it handles the complex data gathering instantly. 1. Your AI agent identifies a business need, such as assessing product sentiment or checking coupon usage. 2. The agent calls the appropriate tool within this MCP—for example, using list_reviews to gather raw customer data. 3. This MCP returns structured JSON data containing everything from review bodies and ratings to question details, which your AI agent then uses for analysis. ## Frequently Asked Questions **How do I use Judge.me MCP to find the most reviewed products?** Use list_products first. This tool returns all product names and, critically, includes an aggregate review count right in the data set, letting you instantly sort for your best sellers or biggest problem children. **Can Judge.me MCP track coupon usage?** Yes. You call list_coupons to retrieve a full list of all active discount coupons. This is useful if you want to audit which incentives are running and whether they expire soon. **What difference does get_review make compared to listing reviews?** list_reviews gives you a summary of everything. But when you use get_review, you pull deep-dive data on one specific review, including its metadata and moderation status, which is key for detailed analysis. **Does Judge.me MCP help with Q&A?** Absolutely. You can run list_questions to see all open inquiries. Then use get_question on a specific ID to pull the full context, helping your agent draft an official, informed reply. **Is Judge.me MCP useful for media analysis?** Yes, it is essential for analyzing user-generated content (UGC). Use list_medias to gather all attached images and videos from reviews, allowing you to analyze the visual feedback alongside the text.