Okendo Reviews MCP. Analyze all product feedback from a single chat window.
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Okendo Reviews connects your AI agent directly to your customer feedback data. Use this server to track store ratings, list recent reviews, pull customer questions and their answers, or view product-specific performance metrics—all through natural conversation.
What your AI agents can do
Get aggregate ratings
Retrieves the overall star rating for your entire store.
Get okendo product details
Gets specific review information and metrics for a single product.
Get question details
Retrieves the full details and context for one customer question using its ID.
Get the current aggregate star rating and total review count for your entire store.
List recent customer questions, get detailed answers, and identify patterns in common inquiries across your site.
Fetch the full metadata for a single review or question using its unique ID. You can also list all reviews and pull associated media like photos and videos.
View product-level review metrics, list all products tracked in Okendo, and retrieve specific product details to benchmark performance.
Fetch high-level configuration data for your entire Okendo instance, useful for technical checks or display setting reviews.
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Okendo Reviews MCP Server: 10 Tools for E-commerce Feedback
Use these tools to access specific metrics, lists of products, customer questions, and individual reviews directly from your AI client.
019d75e3get aggregate ratings
Retrieves the overall star rating for your entire store.
019d75e3get okendo product details
Gets specific review information and metrics for a single product.
019d75e3get question details
Retrieves the full details and context for one customer question using its ID.
019d75e3get review details
Gets complete metadata, including content and rating, for a specific customer review.
019d75e3get store settings
Retrieves the high-level display and configuration settings for your Okendo store instance.
019d75e3list customer questions
Lists recent customer Q&A questions asked on your site.
019d75e3list okendo products
Provides a list of all products currently tracked in Okendo.
019d75e3list question answers
Lists the answers provided for a specific customer question.
019d75e3list review media
Retrieves a list of photos and videos uploaded by customers in their reviews.
019d75e3list reviews
Lists the most recent customer reviews, including ratings and titles.
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What you can do with this MCP connector
Okendo Reviews connects your AI agent straight to your customer feedback data. You pull metrics and details without having to click through a dashboard or navigate complex forms.
Overall Store Health & Configuration
You can get the current aggregate star rating for your whole store using get_aggregate_ratings. It also reports your total review count. For technical checks, you'll find get_store_settings retrieves high-level display and configuration data for your Okendo instance.
Analyzing Product Performance
Need to know how one product is doing? Start by running list_okendo_products to pull a list of every item tracked. Then, you can run get_okendo_product_details on any specific product ID to benchmark its performance and view its unique review metrics.
Review Deep Dives & Media Access
The agent handles all your customer reviews. You'll see the titles and star ratings of the most recent feedback using list_reviews. If you need the full picture, run get_review_details on a specific ID to get complete metadata, including the body content and exact rating. When customers upload media, list_review_media pulls all associated photos and videos from their reviews.
Managing Customer Questions & Answers
If you wanna track common pain points, start by using list_customer_questions to pull a list of recent customer Q&A entries. To get the full context for any specific inquiry, run get_question_details with its ID. You can then use list_question_answers to retrieve all responses provided for that question.
By combining these tools, your agent gives you comprehensive oversight: you pull overall metrics (get_aggregate_ratings), review individual product performance (get_okendo_product_details), and track every piece of customer interaction, from initial questions (list_customer_questions) to detailed media uploads (list_review_media).
How Okendo Reviews MCP Works
- 1 Subscribe to this server and enter your required Okendo Subscriber ID (Store ID).
- 2 Your AI client sends a conversational request (e.g., 'What are the top 3 product complaints?').
- 3 The agent executes the necessary tools (
list_reviews,get_question_details, etc.) and presents you with synthesized, actionable data.
The bottom line is: your AI client talks to Okendo directly. You never have to touch a dashboard.
Who Is Okendo Reviews MCP For?
This is for the e-commerce manager who doesn't have time to manually click through dashboards at 2 am. If you need a quick pulse check on customer sentiment, product weaknesses, or marketing angles, this server gets it to you instantly.
Checks the overall store health by calling get_aggregate_ratings and quickly spotting underperforming products using list_okendo_products.
Identifies viral content or high-value assets by listing customer media via list_review_media, or finding trending topics from questions using list_customer_questions.
Monitors recurring issues by listing recent Q&A, and then fetching specific answers with get_question_details to update documentation.
What Changes When You Connect
- Get instant store health checks. Instead of navigating to the dashboard, just ask for the overall rating;
get_aggregate_ratingsgives you the number immediately. - Pinpoint support gaps instantly. Use
list_customer_questionsto see what customers are asking right now, then useget_question_detailsto see how often it's asked. - Build social campaigns faster. Run
list_review_mediaand you get a clean list of all user-uploaded photos and videos ready for marketing review. - Benchmark product performance quickly. Use
list_okendo_productsto see every item, then runget_okendo_product_detailson the top sellers versus the bottom performers. - Get deep context without clicking. If a customer mentions something vague in a review, use
get_review_detailswith their unique ID to pull all associated metadata.
Real-World Use Cases
Identifying Product Weaknesses
A product owner needs to know why the 'Eco-Sneaker' is getting bad reviews. They ask their agent: 'What are the common complaints for the Eco-Sneaker?' The agent runs get_okendo_product_details, which pulls all specific feedback, allowing the owner to see if the issue is sizing or durability.
Responding to FAQs
A support lead sees a spike in questions about returns. They ask: 'List recent questions related to shipping.' The agent runs list_customer_questions, identifies the trend, and then uses get_question_details for key examples so they can update the help documentation.
Preparing a Marketing Campaign
The marketing team needs fresh content. They ask: 'Show me all media from 5-star reviews.' The agent runs list_review_media, providing the list of assets needed for social proof, bypassing manual downloads.
Checking Store Health Before Launch
A site owner needs a quick overview before an update. They ask: 'What is our store's current overall rating and are there any unanswered questions?' The agent runs get_aggregate_ratings followed by list_customer_questions, giving them two key metrics in one response.
The Tradeoffs
Trying to analyze everything at once
Asking, 'Tell me about our products, reviews, and questions.' The agent gets overwhelmed because the query is too broad, resulting in a massive, unreadable data dump.
→
Break it down. First, run list_okendo_products to identify the product group. Then, use that list to target your review search: 'What are the average ratings for Product X?' This forces the agent to call specific tools like get_okendo_product_details.
Confusing metrics with content
Asking, 'Are people happy or unhappy?' The system might only give a rating (e.g., 4.5 stars) but fail to provide the actual qualitative reasons.
→
Always follow up quantitative data. After running get_aggregate_ratings, ask: 'Give me three recent reviews that explain why it's not a perfect 5.0.' This forces the agent to run list_reviews and pull body text.
Ignoring media assets
Assuming high ratings mean good content, but missing out on visual evidence of quality or flaws.
→
When reviewing a product, always ask: 'What user-uploaded photos are attached to the top 5 reviews?' This uses list_review_media and gives you actionable marketing collateral.
When It Fits, When It Doesn't
Use this server if your primary need is centralized data aggregation. If you want a single conversational interface that pulls metrics (like star ratings via get_aggregate_ratings) and content (like review bodies via list_reviews), this is the right tool. It handles synthesizing those disparate sources.
Don't use it if your problem requires internal system logic or causal links not present in Okendo data. For instance, if you need to know why a product failed due to an operational issue (e.g., 'The warehouse packed it wrong'), this server can only report what the customer said about it. It cannot access CRM notes or inventory logs.
This is purely for reading and structuring existing public-facing feedback data.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Okendo. 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
Dealing with scattered, siloed customer data takes forever.
Today, checking on your store's health means jumping between four tabs: the general ratings dashboard, the product page metrics, the Q&A log, and the raw review list. You copy a rating from one place, then switch to another tab just to find the supporting text for that rating. It’s manual, it takes minutes of clicks, and you risk missing critical details.
With this MCP server, your agent does all that work in seconds. You ask a single question—like 'What is the current overall store rating and what's the most common complaint?' The response aggregates data from `get_aggregate_ratings`, reads recent issues via `list_customer_questions`, and gives you one unified answer.
Okendo Reviews MCP Server: Get insights on ratings, reviews, and Q&A.
You used to manually check for media assets by going into the review section, filtering by 'photos,' and then downloading them one by one. This process was slow and unreliable. You’d end up with dozens of individual files scattered across your desktop.
Now, you just ask: 'List all customer photos from 5-star reviews.' The agent calls `list_review_media` and hands you a clean, structured list of every asset. It's instant, and it gives you everything in one go.
Common Questions About Okendo Reviews MCP
How do I find my Okendo Subscriber ID? +
You can find your Subscriber ID in the Okendo dashboard settings. It is a unique identifier for your specific store instance.
Does this support AI-generated review summaries? +
This implementation focuses on retrieving raw review and Q&A data. If your Okendo plan includes AI summaries, they may be accessible within the detailed review metadata.
Can I see photos uploaded by customers? +
Yes! Use the list_review_media tool to retrieve a list of all visual assets (photos and videos) provided by your customers in their feedback.
When I use the `list_okendo_products` tool, what kind of product data can my agent access? +
The tool lists all products tracked within Okendo. Your AI agent receives a structured summary that includes basic metrics and current review counts for each item.
How do I check the overall setup or display information using `get_store_settings`? +
This tool retrieves high-level configuration details for your entire Okendo account. It's useful for verifying how your social proof is currently displayed to customers.
If I run `list_reviews`, can the agent retrieve reviews from a specific timeframe? +
Yes, you can specify date ranges when calling list_reviews. This lets your AI client pull comprehensive historical data instead of just showing the most recent entries.
What is the workflow for getting answers after listing questions using `list_customer_questions`? +
You first call list_customer_questions to find the topic ID. Then, you pass that specific Question ID to the dedicated answer tool to retrieve all related responses.
When running `get_review_details`, what unique identifier must I provide? +
You must supply the unique review ID for this function to work. Providing this single ID ensures your agent pulls accurate, non-conflicting data about one specific piece of feedback.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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