Refiner MCP. Automate feedback collection from in-app actions.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Refiner connects your customer feedback account to any AI agent. It lets you pull in-product insights and churn signals by running micro-surveys when users hit specific moments—like after a checkout or clicking a feature.
You can manage everything from NPS scores and raw feature feedback right inside your chat interface, keeping your user understanding loop closed without leaving the workspace.
What your AI agents can do
Check refiner status
Verifies the API server's current operational health and status.
Get refiner contact
Retrieves specific contact details for product-related users or entities.
Identify refiner user
Identifies a user by email and allows you to update their technical traits in the system.
The agent pulls survey submissions using list_refiner_responses, allowing you to filter by UUIDs or specific date ranges to find trends.
You can use track_refiner_event to programmatically log a user action, which triggers the delivery of a perfectly timed micro-survey.
The agent uses identify_refiner_user to confirm or update traits for users, ensuring that targeted surveys reach the right people.
You can list user segments with list_refiner_segments to quickly see how your audience is distributed across defined groups.
Use list_refiner_surveys to get a full rundown of all active feedback surveys, including their status and response counts.
The agent checks the server's operational health using check_refiner_status before running any major queries or actions.
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Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Refiner MCP Server: 8 Tools for User Insights
These tools let you manage everything from listing surveys and checking API health to tracking specific user actions and querying detailed feedback submissions.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Refiner on Vinkius019dd14bcheck refiner status
Verifies the API server's current operational health and status.
019dd14bget refiner contact
Retrieves specific contact details for product-related users or entities.
019dd14bidentify refiner user
Identifies a user by email and allows you to update their technical traits in the system.
019dd14blist refiner contacts
Lists all product contacts available within the Refiner account.
019dd14blist refiner responses
Retrieves a list of specific survey submissions, allowing filtering by date or unique identifiers.
019dd14blist refiner segments
Lists defined user segments and allows querying the distribution of users within those groups.
019dd14blist refiner surveys
Retrieves a list of all current feedback surveys, detailing their status and total response count.
019dd14btrack refiner event
Logs a specific user action (an event) for later analysis and potential micro-survey triggering.
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 Refiner, then connect any of our 4,800+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,800+ 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
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Refiner. 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.
VINKIUS INFRASTRUCTURE
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Managed infra
V8 Isolated
Sandboxed per request
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DLP Enforced
Policy on every call
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EU data residency
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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 8 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Pulling insights from user surveys shouldn't feel like running a marathon.
Today, gathering feedback means jumping between three places: the survey platform to see total scores; the analytics dashboard to filter by segment; and then manually exporting everything into a spreadsheet to spot trends. It’s slow, it's tedious, and you almost always lose some data points in the copy-paste process.
With this MCP Server, your agent pulls all that together. You ask for 'Responses from Segment X related to Feature Y,' and the agent runs `list_refiner_responses` combined with segment queries (`list_refiner_segments`). The result lands right in the chat—formatted, filtered, and ready to act on.
Refiner MCP Server: Get a full view of user identity and feedback.
The old way required you to check three separate systems just to know who was giving the feedback, what their status traits were, and if they were part of a specific group. You’d have to manually cross-reference User IDs from one dashboard into another.
Now, when your agent runs `identify_refiner_user`, it confirms the identity. Then you can run `list_refiner_responses` for that user ID, and simultaneously use `list_refiner_segments` to confirm they belong to a high-value group—all in one seamless conversational flow.
What you can do with this MCP connector
You connect your customer feedback account straight into any AI agent. Refiner lets you pull in-product insights and churn signals by running micro-surveys precisely when users hit a certain spot—think right after they complete checkout or click on some core feature. You manage everything from NPS scores to raw feature comments without ever leaving your chat interface, keeping your whole user understanding loop closed.
Monitoring Survey Performance & Data Retrieval
You need to know what's going down with your feedback. Use the agent to get a full rundown of every active survey—whether it's in-app, via email, or linked elsewhere—using list_refiner_surveys. This tool gives you the status and total response count for all of them right away. When you need deep data, run list_refiner_responses to pull up submitted answers.
You can filter these responses by specific date ranges or unique identifiers (UUIDs), so you're spotting trends fast. Before making any big query, you should always check the server’s operational health using check_refiner_status. It confirms the API is running smoothly and ready for action.
Identifying Users & Targeting Feedback
To make sure your surveys land on the right people, you gotta manage user identity. You can use identify_refiner_user to confirm or update specific technical traits for any given user by their email address. This precision is key when you're sending out a micro-survey.
For group insights, you'll want to list and query your defined user segments using list_refiner_segments. This shows how your entire audience is distributed across those groups so you know who you're talking to. You can also manage basic product contact details or run simple user identification checks by listing contacts with list_refiner_contacts or getting specific details with get_refiner_contact.
Driving Action & Analysis
The real power is triggering things. Instead of waiting for users to complain, you make the survey show up at the perfect time. You use track_refiner_event to programmatically log a specific user action—an event in the system—which immediately triggers the delivery of that perfectly timed micro-survey. This lets your agent act as an automated feedback mechanism.
If you need a list of all available survey definitions, run through list_refiner_surveys. When you're ready to query user data, remember that list_refiner_responses handles the retrieval, letting you focus on the content. The combination of tracking an event (track_refiner_event) and pulling responses (list_refiner_responses), after ensuring the right users are targeted (identify_refiner_user), makes your whole feedback process airtight.
019dd14b-a709-7173-b1ac-650581dc3634 
How Refiner MCP Works
- 1 First, subscribe to this server and enter your Refiner API Key (you find that in your project settings).
- 2 Next, tell your agent what you need—for example, 'List all active surveys' or 'Track event Clicked Upgrade for mike@example.com'.
- 3 The agent executes the necessary tool calls (
list_refiner_surveysortrack_refiner_event), pulls the structured data back, and gives you a plain English summary of what it found.
The bottom line is: Your AI client manages your entire feedback system using natural conversation; you never have to leave the chat window to check survey results or log an event.
Who Is Refiner MCP For?
This is for Product Managers who are tired of context switching between analytics dashboards and their chat workspace. It's also for Growth teams that need to automate event tracking, and Customer Success reps who need fast access to individual user feedback histories.
Checks survey results and user sentiment using simple AI commands so they can report on feature adoption without digging through CSVs.
Automates event tracking (using track_refiner_event) and verifies segment metadata directly from the chat to run targeted campaigns.
Monitors individual user feedback submissions via list_refiner_responses and checks identity traits using identify_refiner_user when a client calls in.
What Changes When You Connect
- Stop manually checking survey dashboards. Use
list_refiner_surveysandlist_refiner_responsesto pull status, counts, and specific submissions instantly via a simple chat command. - Never miss a critical user action. By using
track_refiner_event, you log high-fidelity behavioral data that allows the system to trigger micro-surveys at the perfect moment in the user flow. - Keep your audience always accurate. The agent uses
identify_refiner_userandlist_refiner_segmentsso you're only targeting feedback efforts toward verified, relevant groups of users. - Get full operational visibility instantly. If you need to know if the system is running right before a campaign launch, just run
check_refiner_status. It keeps everything reliable. - Process data without leaving your workflow. Instead of copy-pasting user IDs or segment lists, simply ask the agent to query them using tools like
list_refiner_segments.
Real-World Use Cases
The New Feature Launch Check
A PM wants to know how users feel about a new checkout button. Instead of waiting for the manual survey response, they ask the agent to run list_refiner_surveys to confirm the 'Checkout Flow' survey is active. Then, they use track_refiner_event every time a user hits that page, ensuring immediate feedback capture and getting real-time sentiment data.
Debugging User Onboarding
A CS rep gets a complaint about a specific account. They ask the agent to use identify_refiner_user with the user's email. The agent confirms the identity and then runs list_refiner_responses for that user, pulling up all their past feedback submissions in seconds.
Identifying High-Value Users
The Growth team needs to know which users saw a specific beta feature. They use the agent to run list_refiner_segments and query for 'Beta Testers'. This tells them exactly who is in the target group, allowing them to send a specialized follow-up survey.
Auditing System Readiness
Before running a massive data pull, an engineer asks the agent to run check_refiner_status. The agent confirms the API is healthy. Then, they use list_refiner_contacts and get_refiner_contact to verify basic account credentials are solid before proceeding with complex analysis.
The Tradeoffs
Treating feedback as a single database view
Trying to manually stitch together data from 'NPS reports,' 'Segment dashboards,' and 'Event logs' in three different tabs, which is slow and error-prone.
→
Use the agent to consolidate. Run list_refiner_surveys for an overview, then use list_refiner_segments to narrow down the audience, and finish by using list_refiner_responses to pull targeted data in one chat interaction.
Forgetting user context when tracking events
Just sending a generic 'User viewed page X' event without knowing who the user was or what their current status traits were.
→
Always verify identity first. Use identify_refiner_user to set the specific user profile, then execute track_refiner_event. This links the action directly to a known user.
Assuming all data is current
Running an analysis on segment groups (list_refiner_segments) and assuming they reflect real-time changes, only to find out the API hasn't been checked for health.
→
Always start by running check_refiner_status. This confirms the server is ready. Then, proceed with your data list calls like list_refiner_responses.
When It Fits, When It Doesn't
Use this MCP Server if your primary bottleneck is moving feedback analysis from manual dashboard reports into an actionable chat workflow. You need to run micro-surveys that are directly tied to user behavior (i.e., 'Did they click X?'). The core value here is the ability to combine event tracking (track_refiner_event) with segmentation (list_refiner_segments) and immediate data retrieval (list_refiner_responses).
Don't use this if you just need a simple, static list of contacts. For that, list_refiner_contacts is sufficient on its own. Also, don't assume it replaces your core analytics stack; think of it as the interface to your data. If all you care about are basic health checks and nothing else, just running check_refiner_status will suffice. But if you want to build a full feedback loop—from observation (event tracking) to analysis (segment listing) to action (survey response pulling)—this is the tool for you.
Common Questions About Refiner MCP
How do I list all my current feedback surveys using list_refiner_surveys? +
You ask the agent to run list_refiner_surveys. It will return a list of every active survey—like 'NPS - Post Checkout' or 'Beta Feedback'—and tell you how many responses each one has.
Can I track an event for a specific user using track_refiner_event? +
Yes. You provide the agent with the event name and the target user ID. The tool logs that action, which means if you have micro-surveys tied to it, they'll automatically trigger.
What is the difference between list_refiner_contacts and identify_refiner_user? +
list_refiner_contacts shows a general listing of product contacts. identify_refiner_user, however, requires you to pinpoint one user and allows you to update their specific traits for better targeting.
How do I find out if the Refiner API is working right now? +
Just ask the agent to run check_refiner_status. This tool immediately verifies the server's operational status, letting you know if there are any connectivity issues before you start querying data.
Can I query responses for a specific date range using list_refiner_responses? +
Yes. You specify the date range and ideally include technical filters like UUIDs in your request. The agent executes list_refiner_responses and returns submissions matching those criteria.
When using `list_refiner_responses`, can I filter submissions by unique identifiers like UUIDs or technical traits? +
Yes, you pass the specific ID criteria to the tool call. This lets your agent pull an exact record rather than just searching within a date range. You can pinpoint single responses instantly.
If I change a user's status or role, how do I use `identify_refiner_user` to make sure the update sticks? +
The command writes the changes directly to the user profile. This ensures that any subsequent segment checks or micro-surveys targeting those specific traits will see the new data immediately.
When I run `list_refiner_segments`, am I seeing live data, or are these segments based on historical definitions? +
You are viewing the current segment definition and its associated user count. The tool queries the most up-to-date state of your audience distribution across all recorded events.
Can I check the responses for a specific survey via AI? +
Yes! Use the list_refiner_responses tool and provide the Survey UUID. Your agent will retrieve the latest submissions, which you can then ask the AI to summarize or filter by date.
How do I identify a user and add custom traits using the agent? +
Use the identify_refiner_user action. Provide the User ID or Email and a JSON string of traits (e.g., '{"plan":"pro"}'). This helps you target surveys based on specific user metadata.
Is it possible to track a custom event to trigger a survey via AI? +
Absolutely. Use the track_refiner_event tool. Provide the event name and the user's ID/Email. When this event is logged, Refiner will trigger any surveys you have configured for that specific action.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.