Judge.me MCP. Analyze every piece of customer feedback, instantly.
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…and any MCP-compatible client
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Judge.me MCP Server manages your entire product review lifecycle. Get product details, fetch individual reviews, list customer questions, and track answers all in one place.
It's built to let your AI agent analyze customer sentiment, audit coupon usage, or pull media content from your e-commerce shop directly via the Judge.me API.
What your AI agents can do
Get product
Retrieves detailed information for a specific product.
Get question
Fetches the full details of a specific customer question.
Get review
Returns deep information on a single review, including its metadata and moderation status.
Your agent can fetch a list of all product reviews, analyze specific review details, and see all media attached to customer content.
The agent lists all questions and answers on your site, allowing you to identify unresolved customer queries.
You can list all active discount coupons and check shop settings to understand your marketing and system configurations.
The agent retrieves a list of all products, including their names and aggregate review counts, to focus analysis.
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Judge.me MCP Server: 10 Tools for E-commerce Feedback
These tools let your AI agent pull structured data on product reviews, customer questions, coupons, and shop settings from the Judge.me platform.
019d75beget product
Retrieves detailed information for a specific product.
019d75beget question
Fetches the full details of a specific customer question.
019d75beget review
Returns deep information on a single review, including its metadata and moderation status.
019d75beget settings
Retrieves general shop settings for Judge.me, useful for checking system configuration.
019d75belist answers
Lists all answers given to customer questions, helping you audit if inquiries have been resolved.
019d75belist coupons
Lists all currently active discount coupons available in the shop.
019d75belist medias
Lists all images or videos uploaded by users and attached to reviews for content analysis.
019d75belist products
Lists every product in the shop, including names and total review counts.
019d75belist questions
Lists all customer questions asked, providing the text, status, and IDs for easy tracking.
019d75belist reviews
Lists all product reviews, providing reviewer names, ratings, titles, and body text.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Judge.me, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Your AI agent handles your entire product review process. Instead of messing with your e-commerce dashboard, your agent uses these tools to pull, analyze, and track all customer feedback. You can check product details using get_product, or pull deep info on a single review with get_review. It even grabs any media attached to a review using list_medias.
You can list every product in the shop and get their names and total review counts via list_products. You can list every customer question with list_questions, and pull full details on one question with get_question. To audit conversations, your agent lists all answers given to customer questions using list_answers. You can also list all active discount coupons with list_coupons, and check general shop settings with get_settings.
Your agent pulls a list of all product reviews using list_reviews, giving you names, ratings, titles, and body text for every submission. You can retrieve general shop settings using get_settings to see your system configuration.
How Judge.me MCP Works
- 1 Your agent calls
list_productsto get a list of all items in your shop. - 2 The agent then calls
list_reviewsusing the product IDs to pull all customer feedback for those items. - 3 Finally, the agent calls
get_reviewfor a specific ID, giving you the full metadata and body of the review.
The bottom line is that your AI agent treats your entire e-commerce review system like a data source, pulling specific pieces of information without needing a human to click through any dashboards.
Who Is Judge.me MCP For?
This is for the marketing manager who needs to prove ROI from social proof. It's for the e-commerce analyst who has to manually pull review metrics into a spreadsheet. It's for the product owner who needs to quickly audit customer sentiment across hundreds of products. If your job involves analyzing customer voice, this tool saves hours of manual data collection.
Uses list_products to scope the analysis, then calls list_reviews and list_questions to pull raw data for quarterly performance reports.
Employs get_review and list_medias to pull example customer content, figuring out what kind of photos or testimonials resonate best.
Uses list_coupons and get_settings to audit the current incentive program and ensure coupon codes are working correctly.
What Changes When You Connect
- Audit Customer Sentiment: Instead of wading through dashboard filters, use
list_reviewsandlist_questionsto pull comprehensive, raw data on customer feedback. Your agent analyzes thousands of reviews in seconds. - Track Customer Service: Need to know if a question was answered? Call
list_answersandlist_questions. Your agent compiles a full service history for a given product ID, showing open loops immediately. - Analyze UGC Content: Don't just read the text. Use
list_mediasto pull all user-generated content (images, videos) attached to reviews, letting your agent categorize and analyze visual proof. - Scope Your Analysis: Start broad by running
list_products. This tool gives your agent a complete inventory of every item and its total review count, so you never forget a product line. - Manage Incentives: Quickly check your shop's financial health.
list_couponslets your agent list all active discount coupons, whileget_settingschecks the overall system configuration. - Deep Data Dive: Need to know exactly why a review was flagged? Use
get_reviewwith a specific ID. The tool returns deep-dive information, including moderation status, allowing for precise data validation.
Real-World Use Cases
Identifying product weaknesses from reviews
A product manager needs to know which features get the most complaints. They ask their agent to run list_products first. Then, they run list_reviews and feed the data into a sentiment model, allowing the agent to group recurring negative keywords and pinpoint the exact problem area.
Handling a PR crisis with customer questions
A store suddenly gets bad press. The marketing team asks their agent to run list_questions to see the volume and nature of the complaints. They then use list_answers to see if the customer service team has provided a consistent, official response across all queries.
Launching a new product line and tracking buzz
The product team wants to gauge initial interest. They run get_product for the new item. Then, they run list_reviews and list_medias to pull all early feedback and visual proof, giving them an instant social proof metric for pre-launch PR.
Auditing coupon usage and shop setup
The ops team suspects someone is abusing the discount system. They ask their agent to run list_coupons to see active codes, and then get_settings to check if the coupon rules are configured correctly on the backend.
The Tradeoffs
Trying to find one review's context
A user sees a review and manually copies the text. They then have to switch tabs to find the product details, the media, and the original question. This takes 5 minutes of clicking.
→
Just let your agent run get_review with the review ID. The tool returns everything in one payload: the full review text, the product ID, the associated media, and the metadata needed for context.
Needing to know if a question was answered
A team member checks the Q&A page, finds a question, and then has to click through multiple pages to see if any answer exists. They might miss older, non-flagged replies.
→
Run list_answers. This tool pulls a list of all responses, letting your agent audit the entire conversation history for that question ID, ensuring nothing is missed.
Finding all products that have low reviews
Manually exporting product lists and filtering them in Excel to find items below a certain review count. This is tedious and prone to human error.
→
Run list_products. The tool provides a clean list of all products and their aggregate review counts, letting your agent filter and sort this data immediately for gap analysis.
When It Fits, When It Doesn't
Use this server if your primary goal is deep, systematic analysis of customer-generated text and metadata. You need to pull raw data—everything from review bodies to coupon IDs—to feed into a model, a report, or a decision workflow. The key is the breadth of data: reviews, Q&A, media, and settings.
Don't use this if you just need to check a single product's current inventory stock level. For that, you'd need a dedicated inventory API. Also, if you only care about displaying reviews on a simple website widget, the API might be overkill. But if you need to analyze the data (e.g., 'Which product gets the most complaints?'), this is exactly what you need.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Judge.me. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manual customer feedback collection is a massive time sink.
Today, getting a full picture of customer sentiment means logging into the Judge.me dashboard. You click on a product, then you scroll through reviews. If you want to know about a question, you go to the Q&A tab. If you want to see the photos people attached, you're clicking through media folders. You end up copying and pasting data into a spreadsheet, and half the time, you miss the context.
With the Judge.me MCP Server, your agent handles the whole process. It calls `list_reviews` to get the names and ratings. Then, it calls `list_questions` to find the pain points. It’s all structured data pulled directly into your workflow. You get the full dataset, instantly.
Judge.me MCP Server: Analyze reviews and questions with precision.
Before, if you wanted to check if a specific coupon was active, you had to find the coupon section, navigate the settings, and manually read the list. If you wanted to know the product's overall performance, you had to count reviews across multiple views.
Now, your agent can run `list_coupons` to get a clean list of active rewards, and `list_products` to get the total review count for every item. It cuts the manual auditing process down to two commands.
Common Questions About Judge.me MCP
How do I use the list_reviews tool to check sentiment? +
The list_reviews tool pulls the reviewer name, rating, title, and body for all products. You can feed the body text directly into your sentiment model for analysis.
Can I use get_review to see media attached to a review? +
The get_review tool returns deep-dive data, but you should use list_medias to get a comprehensive list of all images and videos attached to reviews across the shop.
What is the difference between list_questions and list_answers? +
Use list_questions to see all customer inquiries. Use list_answers to see all responses that have been officially posted to those questions, helping you audit the resolution status.
Do I need get_product before listing reviews? +
No. You can run list_products first to get the list of all products, and then use those IDs to scope your review analysis with list_reviews.
How do I check for active discount coupons using list_coupons? +
The list_coupons tool retrieves a list of all active discount coupons. This is useful for verifying that your incentive programs are correctly configured and running.
How does `list_products` help me find which product needs review analysis? +
It lists all products in the shop, providing names, IDs, and aggregate review counts. This helps you quickly identify which products have the most or least customer feedback to investigate.
When should I use `list_medias` to analyze user-generated content? +
You use it when you need to analyze images or videos attached to reviews. It lists all media attached to reviews, letting you examine the actual user-generated content.
What is the difference between `get_question` and `list_questions`? +
list_questions gives you a comprehensive list of all customer questions. get_question retrieves the specific details for one question, letting you prep an official answer.
How do I get Judge.me API credentials? +
Log in to your Judge.me account, navigate to Settings > Integrations > Developers, and find your Private API Token. You also need your shop's primary domain.
Does it support customer questions? +
Yes, you can list and retrieve customer questions and answers using the corresponding tools in this MCP.
Can I see review media? +
Yes, the list_medias tool allows you to retrieve images and videos that customers have attached to their reviews.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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