Zengain MCP for AI. Pinpoint high-intent leads before they talk to support.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Zengain MCP connects your customer success and usage analytics data to any AI client. It helps you identify high-intent website visitors by company, letting your agent pinpoint warm leads before they even fill out a contact form or send an email.
What your AI can do
Get health score
Calculates the current customer health score for a specific user based on their engagement history.
Get product
Fetches detailed configuration metadata for a single, named product within your account.
Get analytics summary
Retrieves a high-level summary of overall product usage metrics and key performance indicators.
Get an overall summary of usage metrics, calculate individual customer health scores, or retrieve detailed information on specific product configurations.
List registered users and track custom events in real-time to see exactly what actions people are taking inside your platform.
List configured webhooks so you can understand how data flows out of the system and where external tools are pulling information from.
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Zengain: 10 Tools for Product Intelligence
These tools allow you to programmatically pull everything from product lists and user profiles down to granular usage events, giving your agent a deep view of customer activity.
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 Zengain on VinkiusGet Health Score
Calculates the current customer health score for a specific user based on their engagement history.
Get Product
Fetches detailed configuration metadata for a single, named product within your...
Get Analytics Summary
Retrieves a high-level summary of overall product usage metrics and key performance...
Get User Details
Pulls comprehensive information and profile data for an individual user ID.
List Events
Retrieves a list of all custom tracking events that have occurred across the...
List Kpms
Lists and checks Key Product Milestones to help identify successful onboarding stages or potential churn risks.
List Products
Generates a list of all products currently registered in your Zengain account.
List Users
Provides an immediate listing of every user associated with the product.
List Webhooks
Shows all configured webhooks, letting you audit where external systems are...
Track Event
Records a new, specific custom event (like 'Feature X clicked') to update the user's...
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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 Zengain, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ 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
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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 connection provides 10 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manual dashboard review is a time sink.
Today, figuring out who needs help takes clicking through six different tabs: the general analytics dashboard for MAU counts; then jumping to the user list to find specific accounts; and finally opening deep-dive reports just to check feature adoption rates. It's a miserable process of copy-pasting numbers into a spreadsheet that always leaves you feeling like you missed something critical.
With this MCP, your agent handles all those tabs in one conversation. You don't look at dashboards; you ask questions. The system pulls the required data—like running `get_analytics_summary` and checking `list_kpms`—and gives you a clean, conversational answer. What you get is instant diagnostic power.
The Zengain MCP provides real-time user status.
You no longer have to manually check if an account is active or what features they are using. You can ask your agent to run `get_health_score` and immediately know the customer's current standing, even pulling in details about specific product configurations via `get_product` for context.
This means you move from reactive support—answering 'What happened?'—to proactive account management. The system provides a clear, single source of truth on user engagement.
What your AI can actually do with this
Stop guessing about why users churn. This connector gives your agent deep insight into how people are actually using your product and interacting with it. Instead of logging into multiple dashboards to piece together a user's journey, you can ask questions like, 'What is the health score for this account?' or 'List all products we offer.' The system pulls real-time usage metrics, showing which features users adopted and where they might be struggling.
You can track key product milestones (KPM) and monitor custom events to build a complete picture of engagement.
When you need to automate outreach based on these findings—for example, sending an alert when a user's health score drops or if critical usage activity stops—the real power comes from combining this data. You can chain your agent with other MCPs in the Vinkius catalog; for instance, linking this analytics feed directly to a messaging platform’s MCP lets you automatically draft and send outreach to that warm lead right through your AI client's natural conversation flow.
019dd18e-0fd0-7064-9df3-feae23769dd3 Here's how it actually works
The bottom line is you get deep, actionable insights into your customer base without jumping between tabs and dashboards.
Subscribe to this MCP, then supply your Zengain Tenant ID and API Key.
Connect it to your preferred AI client—like Claude or Cursor—from a single place.
Ask your agent any question related to user activity, product performance, or usage trends.
Who is this actually for?
Product Managers who dread manual dashboard review; Customer Success Managers fielding too many 'why isn't it working?' calls; Growth Marketers needing proof of user intent.
Uses this to check a user's health score and list key product milestones before calling them, knowing exactly what features they haven't used recently.
Runs analytics summaries and lists all registered products to identify which features are lagging in adoption or need better onboarding flows.
Uses this to list users and track custom events, segmenting leads who show high-intent activity versus those who went quiet.
What Changes When You Connect
Stop guessing about lead quality. Use get_health_score to get a single, immediate number that summarizes how engaged a user is, rather than reviewing dozens of scattered metrics.
Know exactly what the customer is doing at all times. You can track custom activity using track_event, creating an audit trail for every click or action taken in the product.
Understand your full inventory. Use list_products and get_product to keep a precise record of every service offering, ensuring nothing gets overlooked during onboarding.
Audit data flow easily. By running list_webhooks, you see exactly which external systems are connected, eliminating the fear that critical data is leaking or inaccessible.
Get clear visibility into user status. The combination of list_users and get_user_details lets your agent pull profiles fast, giving context to every interaction.
Manage product growth cycles. Running list_kpms helps you spot when a new customer hits a major milestone, signaling the perfect time for a high-value outreach.
See it in action
The sudden drop in activity
A CSM notices a key user hasn't logged in this week. Instead of sending a generic 'checking in' email, they ask the agent to run get_health_score and review recent events via list_events. They discover usage dropped after product feature Y was updated. The CS manager can then follow up with highly specific training on Feature Y.
Segmenting high-value leads
A Growth Marketer wants to find users who interacted with the pricing page multiple times but never signed up. They run list_users and filter by activity, then use get_user_details to pull their company info for targeted outreach.
Debugging a broken integration
The ops engineer suspects data isn't making it from the platform to the CRM. They first run list_webhooks to check if any hooks are configured, then use list_events to verify that the core action event is actually being generated.
Onboarding a new enterprise client
The Product Manager needs to confirm all necessary services are active. They run list_products for an inventory check, then use get_product on the specific 'Enterprise Tier' to ensure all required metadata is present before signing the contract.
The honest tradeoffs
Treating analytics like a spreadsheet
Copy-pasting 50 user emails into a separate CRM list and then manually assigning them to sales reps. This is slow, error-prone, and always behind the curve.
Use your AI client to query list_users and filter by specific criteria (e.g., 'users with health score below 50'). Then, use that list directly in a multi-MCP workflow to trigger an automated task.
Relying on generalized reporting
Asking for 'overall monthly usage' without knowing which specific feature caused the dip. The report is too broad and doesn't pinpoint root causes.
Use list_kpms to narrow the focus. Ask, 'Which Key Product Milestones did users who signed up last month fail to achieve?' This gives an actionable gap.
Ignoring user context
Sending a generic feature announcement email to every single user, even those who just used that exact feature five minutes ago. It's spammy and annoying.
First, use get_user_details to pull the user’s history. Then, ask your agent to check if they have logged any events related to the new feature using list_events. Only send the message if needed.
When It Fits, When It Doesn't
Use this MCP when your core problem is understanding why a customer's usage pattern changed or identifying which leads are truly ready for outreach. You need historical data combined with current state metrics, not just simple counts. For instance, if you only need to know 'How many users signed up yesterday?' then list_users works. But if you need to know 'Which of those 10 users have a health score below 60 AND haven't used Feature X in the last week?', this MCP is necessary. Don't use it just because you can; use it when you need diagnostic depth, combining list_users, get_health_score, and list_events together.
Avoid using this if your only goal is simple data transfer (e.g., moving a static list of names). For those cases, a basic API connector might suffice. This MCP's value lies in its ability to analyze the relationship between different pieces of data—the connection between product usage and customer health.
Questions you might have
How do I use the get_health_score tool with Zengain? +
You provide the specific customer ID to the agent; it returns their calculated health score and a brief explanation of why that number was generated. This is better than looking at raw usage numbers because the system weights them for you.
Can I use list_events with Zengain MCP? +
Yes, list_events lets you see a full history of custom actions taken by users. This is critical because it shows when something happened, not just that it happened.
Is get_user_details the same as list_users? +
No; list_users gives you a basic roster of all IDs. You must then use get_user_details on an individual ID to pull rich data like their profile information or company details.
What is the best way to track user behavior? +
You should combine tools. Use list_users to find who you care about, then use get_user_details for context, and finally, track_event manually when a key interaction happens that needs logging.
How do I check which webhooks are active using the `list_webhooks` tool? +
You use this MCP to see all configured endpoints and their status. It provides a list of registered hooks, allowing you to verify if your data flow connections are live or need updating.
Can I refine my user search when using the `get_user_details` tool? +
Yes, you must provide specific identifiers like a unique user ID or email address. This ensures the agent pulls precise records for that single user instead of loading broad lists.
What format does the `track_event` tool require to log a custom event? +
The tool requires three pieces of data: an event name, a timestamp, and a metadata payload. You define the event in natural language and include key/value pairs for context like user IDs or feature names.
If I need to process a large number of users, is there a limit when using the `list_users` tool? +
The MCP handles pagination automatically for bulk reads. When you run this tool, it returns results in manageable chunks until all available user records are processed by your agent.
Can I calculate a customer's health score using my AI agent? +
Yes! Use the get_health_score tool by providing the User ID. The agent will retrieve the real-time engagement score from Zengain.
How do I see high-level usage summary for a specific product? +
Use the get_analytics_summary tool. You can optionally provide a Product ID to filter the metrics for that specific offering.
Is it possible to track a new custom event through this integration? +
Yes, use the track_event action. Provide the User ID and the Event Name to record engagement data programmatically.
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