Fera.ai MCP. Analyze customer feedback, ratings, and UGC in chat.
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Fera.ai MCP Server manages all your customer feedback, ratings, and UGC data. Connect your AI agent to list reviews, check product ratings, and access customer profiles directly.
Stop exporting data to a spreadsheet; pull real-time social proof metrics into your conversation.
What your AI agents can do
Get account info
Retrieves your Fera account and subscription details.
Get customer
Gets specific details for a customer profile.
Get me
Checks the current API token's identity information.
The agent fetches a list of reviews for your store and provides detailed sentiment metadata for specific feedback.
The agent queries a product's aggregated rating and total review count to gauge overall catalog performance.
The agent lists and inspects all customer-submitted photos and videos, making your visual social proof accessible.
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Fera.ai MCP Server: 12 Tools for Reviews and Media
Use these tools to manage, query, and analyze all your customer feedback, ratings, and media assets directly through your AI agent.
019d7598get account info
Retrieves your Fera account and subscription details.
019d7598get customer
Gets specific details for a customer profile.
019d7598get me
Checks the current API token's identity information.
019d7598get product rating
Gets the aggregated rating and total review count for a product.
019d7598get review
Gets specific details for a single customer review.
019d7598list customers
Lists all customer accounts that have submitted feedback.
019d7598list external integrations
Lists active connections to platforms like Shopify or Wix.
019d7598list media
Lists customer-submitted photos and videos (UGC).
019d7598list products
Lists all products currently being tracked by Fera.
019d7598list reviews
Lists all customer reviews across your store.
019d7598list site content
Lists social proof content and widgets available on your site.
019d7598list stores
Lists all stores managed under your main account.
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 Fera.ai, 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 client can pull all the social proof data right into the conversation. It's got direct access to everything—reviews, ratings, and customer photos—so you don't gotta export anything to a spreadsheet. You can list every customer review and check the sentiment metadata for specific feedback. You can check a product's aggregated rating and total review count to gauge overall catalog performance.
You can list and inspect every customer-submitted photo and video, making your visual social proof readily available. You can list all customer accounts that have submitted feedback, and you can get specific details for any customer profile. You can list all products tracked by Fera, and you can get specific details for any product rating.
You can get specific details for a single customer review. You can list all customer reviews across your store. You can list all stores managed under your main account. You can list all customer-submitted photos and videos. You can list social proof content and widgets available on your site. You can list active connections to platforms like Shopify or Wix.
You can retrieve your Fera account and subscription details. You can check the current API token's identity information.
How Fera.ai MCP Works
- 1 Subscribe to the Fera.ai server and provide your private API Secret Key.
- 2 Your AI agent connects to the server via the MCP protocol.
- 3 You prompt your agent with a goal (e.g., 'What are the latest reviews for SKU-123?'). The agent runs the necessary tools and returns the structured data.
The bottom line is, your AI agent acts as a single pane of glass for all your customer social proof data.
Who Is Fera.ai MCP For?
E-commerce Managers who hate manual data exports. Customer Support Agents who need quick, detailed review lookups during a chat. Marketing Analysts who need raw UGC and review data piped directly into their performance workflows.
Checks for new product reviews and rating changes without having to download and reconcile dashboard exports.
Looks up a specific review's details or a customer's profile immediately when a customer mentions their feedback.
Pulls raw UGC and review data into an AI performance workflow for sentiment trending and content ideation.
What Changes When You Connect
- Review orchestration: List all customer reviews and get detailed sentiment metadata. You don't have to manually export reviews to track sentiment changes.
- Rating intelligence: Query aggregated product ratings and review counts in one prompt. You instantly see catalog performance without opening a dashboard.
- UGC monitoring: List and inspect customer photos and videos. This gives you direct access to visual social proof, which is key for marketing campaigns.
- Customer insights: Get detailed customer profiles using the
get_customertool. This lets you personalize engagement when replying to a specific review. - Multi-Store Management: The
list_storestool lets you query data across multiple partner locations. You manage all your feedback from one place. - Integration Audit: Use
list_external_integrationsto monitor which platforms (Shopify, BigCommerce) are connected. You keep track of your tech stack status.
Real-World Use Cases
Addressing a specific customer complaint
A support agent chats with a customer who mentions a product issue. Instead of asking the customer to email screenshots, the agent uses the get_review tool to pull up the exact review details and sentiment, instantly addressing the concern and improving the chat experience.
Analyzing seasonal product performance
A marketing analyst wants to know if the holiday campaign worked. They prompt the agent to run get_product_rating for the top 10 SKUs, getting average ratings and review counts immediately. This data feeds directly into their performance report.
Building a content marketing strategy
A content creator needs fresh ideas for social media. They ask the agent to run list_media to pull raw UGC (photos and videos). This raw material becomes the basis for new ad copy and blog posts.
Auditing e-commerce platform connections
The ops engineer needs to know if a new store was linked correctly. They use list_external_integrations to check the status of Shopify and Wix. This confirms the data flow is working before the store goes live.
The Tradeoffs
Using a spreadsheet for data tracking
Manually downloading review data from the Fera dashboard and pasting it into Excel to calculate sentiment averages or cross-reference multiple store data points. This takes hours and is prone to copy/paste errors.
→
Ask your agent to run list_reviews and then get_product_rating. This pulls the raw, combined data and the aggregate metrics directly into the chat, letting you calculate everything instantly.
Ignoring store scope
Trying to find a review for a subsidiary store by manually checking each individual dashboard, hoping to find the correct account ID. This is slow and almost guaranteed to miss data.
→
Use the list_stores tool first to list all locations. Then, use the list_reviews tool and specify the store ID to get all relevant data in one go.
Treating the API as a list of data endpoints
Calling list_customers and then calling get_customer separately for every single customer ID found. This results in dozens of sequential, slow calls and makes the workflow brittle.
→
First, use the list_customers tool to get the list. Then, structure your prompt to ask the agent to consolidate the details for a specific group of users, minimizing redundant calls.
When It Fits, When It Doesn't
Use this if you need to combine multiple data points—like a customer's profile (get_customer) with the specific reviews they left (get_review) and the overall product performance (get_product_rating)—in a single, conversational query. It’s best for deep analysis and cross-functional reporting.
Don't use this if your only goal is to fetch a single, simple list (e.g., just listing product names). For that, a simple data retrieval API might be cleaner. But if you need context, depth, and the ability to chain together different data types, Fera.ai gives you that power.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Fera.ai. 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 12 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Tracking social proof shouldn't require jumping between dashboards.
Today, tracking customer feedback is a multi-tab nightmare. You start on the main dashboard to see overall ratings, then you jump to the reviews tab to read comments, and finally, you have to open a separate spreadsheet just to pull raw UGC photos and videos for a marketing team. It's copy/paste hell.
With the Fera.ai MCP Server, you talk to your agent. You ask, 'Show me the latest reviews and the associated UGC for the top-rated product.' The agent runs the tools and hands you the combined data, complete with sentiment metadata. It's all in one chat window.
Fera.ai MCP Server: Manage Reviews & Media
Manual processes include exporting product lists, checking which stores are connected via `list_external_integrations`, and then running a separate query for every single customer's review history. This takes hours of low-value data wrangling.
Now, your agent handles the complexity. You ask it to audit your setup, and it runs `list_stores` and `list_external_integrations` in sequence. It gives you a single, clean report on your entire operational status. It’s that simple.
Common Questions About Fera.ai MCP
How do I use the `list_reviews` tool with Fera.ai MCP Server? +
The agent runs list_reviews to get a list of all customer feedback for your store. You can then follow up by asking the agent to run get_review on a specific review ID to pull the full text and sentiment details.
Can I check product ratings using the `get_product_rating` tool? +
Yes, simply tell your agent to check the rating for a product SKU. The get_product_rating tool returns the aggregated rating and the total count of verified reviews for that product.
What is the purpose of the `list_media` tool? +
The list_media tool lets you see all the photos and videos customers submitted. This is your raw, visual social proof, perfect for content creation.
How does the `get_customer` tool help with personalized support? +
The get_customer tool pulls a specific customer's profile details. You can use this data to reference their purchase history or past interactions when responding to their current feedback.
Does the Fera.ai MCP Server support multiple stores? +
Yes. Use the list_stores tool to see all stores under your account, and then use other tools like list_reviews to scope the data by the specific store ID.
How do I use the `list_external_integrations` tool to check my setup? +
This tool lists every external platform connected to your Fera.ai account. It confirms active integrations like Shopify, Wix, and BigCommerce, so you know exactly where your social proof data is flowing.
What is the `get_me` tool for checking my access and limits? +
The get_me tool verifies your current API token identity and subscription details. You can check your account status and ensure your credentials are valid before running large data pulls.
Can I use the `get_account_info` tool to manage multiple business accounts? +
Yes, the get_account_info tool provides access to data across all stores managed under your partner or business account. This allows you to manage social proof data centrally.
How do I obtain my Fera.ai Private API Key? +
Log in to your Fera.ai dashboard, navigate to Configuration > API Keys, and look for your 'Secret Key'. Do not share this key with anyone.
Can I see photos submitted by customers? +
Yes! Use the list_media tool to retrieve all user-generated photos and videos submitted alongside reviews for your store.
Does this work with my Shopify store? +
Absolutely. Fera.ai integrates deeply with Shopify. As long as you provide the API key for your Fera account, you can manage reviews for your Shopify-connected stores.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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