Keen MCP. Analyze event streams and metrics with conversation.
Keen MCP connects your AI agent directly to event streams and analytics data. Instead of building complex dashboards or writing SQL, you talk to the system and get metrics back instantly. Use this MCP to record custom events, count total actions, calculate averages, and pull specific business insights from all your application activity.
Give Claude and any AI agent real-world access
Send specific records detailing user actions (like clicks or purchases) to designated collections.
Determine the total number of events that occurred, or how many distinct values exist for any given property.
Run calculations to find the arithmetic average or the total sum of numeric properties across your event data.
Retrieve configuration details, list all existing collections, and see saved queries within your Keen project.
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What AI agents can do with Keen: 10 Data Aggregation Tools
These ten tools allow your AI agent to track user behavior, count events, and perform complex mathematical calculations on event data streams.
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 Keen MCPAverage Property
Calculates the mathematical average of a specified numeric property across events.
List Collections
Retrieves a list of all event data collections available in your project.
Count Events
Returns the total number of events recorded within a specific collection.
Count Unique
Counts how many distinct, non-repeating values exist for a chosen property.
List Datasets
Displays all cached datasets that have been saved or generated within your project.
Get Project Details
Retrieves comprehensive metadata and configuration details for the entire Keen project.
Record Event
Sends a custom event payload to a specified collection, logging user actions immediately.
List Saved Queries
Shows all previously saved analytical queries you can reference by name.
Select Unique
Lists all unique values found for a specific property, helping understand data...
Sum Property
Calculates the total sum of numeric values associated with a chosen property.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
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 each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Keen, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Keen. 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 CLOUD
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on each call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
The Dashboard Fatigue
Today, checking your app's health means opening five different dashboards: one for signups, one for purchases, one for views, and three more just to cross-reference user IDs. You end up copy-pasting numbers between sheets, hoping none of the metrics are based on slightly different data cuts.
With this MCP, you stop clicking through tabs. Instead, your agent talks directly to the source. Ask it what the total count of successful signups was and the average purchase value for that group—it gives both answers in one go.
Keen MCP: Real-Time Analytics
You no longer need to wait until the end of the week to get a report. The moment an event happens, you can use `record_event` to log it and then immediately use tools like `count_events` or `sum_property` to see the impact in real time.
The difference is that your insights are live, immediate, and conversational. Your agent gives you actionable answers, not just pretty graphs.
What Keen MCP does for your AI
Need to understand what's happening inside your app without pulling up a dozen different dashboard tabs? This MCP lets you do that using nothing but natural conversation. You connect it through Vinkius and give your AI client access to all your event data, treating it like a live chat window for business intelligence.
It’s built to handle everything from basic activity tracking—sending custom records the second they happen—to deep dives into performance metrics. Your agent can count total users who completed a step, calculate the average purchase value across different regions, or find out how many unique product IDs were viewed last month.
This turns complicated data analysis into simple prompts.
019d75c0-0c22-72b7-9ffc-070be48afb2c How to set up Keen MCP
The bottom line is that you get immediate answers about your application's performance without ever touching a database console or writing complex code.
First, subscribe to this MCP and provide your unique Keen Project ID and Master Key.
Next, connect your AI agent via any compatible client. Your agent now has access to all the event data tools.
Finally, ask your agent a question like, 'What was the average order value last week?' and it runs the query for you.
Who uses Keen MCP
This MCP is essential for product managers, data analysts, and developers who spend too much time manually querying databases just to answer simple business questions. It lets you get insight into user behavior immediately.
They use this to run ad-hoc metrics on user funnels—like finding the total count of users who viewed a product page but didn't add it to their cart.
They test new data streams and verify collection schemas instantly using natural language prompts, speeding up debugging cycles.
They pull reports on key performance indicators (KPIs), such as calculating the average revenue per user or summing total daily transactions.
Benefits of connecting Keen MCP
Stop writing boilerplate SQL queries. Instead, ask your agent for the sum_property of revenue last quarter; it runs the calculation instantly, giving you immediate answers without code.
Never get lost in dashboards again. You can use this MCP to first run a list_collections command and see exactly what data streams are available before asking any questions.
Quickly verify your app's status by using record_event. This lets you test if custom event logging is working correctly without needing a dedicated development environment.
Get deep insights into user behavior. Running select_unique shows you every unique value for a property, letting you understand data distribution beyond just counts.
It saves time by automating the discovery phase. You can use list_saved_queries to see what analysis has already been done and reuse those results instead of starting from scratch.
Keen MCP use cases
Debugging a new feature launch
A developer needs to confirm that the 'checkout' funnel is logging events correctly. They ask their agent to run record_event with mock data, and then immediately use count_events on the collection to verify the entry exists.
Quarterly business review prep
A BI Manager needs total revenue figures. They prompt their agent for the sum_property of the 'price' field, getting a precise number without ever opening the backend database console.
Identifying user bottlenecks
A Product Analyst wants to see if users are viewing specific product types. They ask their agent to use count_unique on the 'product_category' property, immediately spotting an underutilized category.
Checking project health and scope
A CTO needs a quick overview of data assets. They prompt for get_project_details, instantly seeing the overall status and metadata of every integrated system.
Keen MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Guessing the right metric to query
Trying to run a complex calculation without knowing if the data is properly structured or what properties exist. This often leads to vague error messages and wasted time.
Before querying, first use list_collections to see all available streams, then check get_project_details for metadata, narrowing your scope before asking for sums or averages.
Assuming data integrity
Running a count and assuming every row is valid. Sometimes, you only need to know how many distinct records exist.
If you're counting users or IDs, use count_unique instead of just running count_events. This gives you the true number of unique entities.
Manual data aggregation
Downloading CSV dumps and spending hours in Excel calculating totals and averages. The data is stale by the time you finish.
Ask your agent to run sum_property or average_property directly through the MCP. You get real-time, accurate numbers instantly.
When to use Keen MCP
Use this MCP if your core problem is transforming raw event data into measurable business insights without writing code. If you need to know 'how many,' 'what's average,' or 'what's the total sum,' this tool works. Don't use it, though, if all you need is a simple list of names; for that, basic search tools might be enough. You also shouldn't rely on it for complex data transformations (like joining three separate tables). This MCP is designed to read and analyze what already exists in your event streams using count_events, sum_property, or average_property. If you need full SQL control, use a dedicated database connector; otherwise, this gives you the power of analytics via conversation.
Frequently asked questions about Keen MCP
How do I start tracking new user actions with Keen MCP? +
You use the record_event tool. You tell your agent what data to send—like a 'login' event and the associated user ID—and it writes that record directly to the correct collection.
Can I calculate revenue totals using Keen MCP? +
Yes, you use sum_property. You simply ask your agent for the sum of a property like 'price' across all relevant events. It handles the aggregation automatically.
What is the difference between count_events and count_unique in Keen MCP? +
The count_events tool tells you how many total actions happened; count_unique tells you how many distinct values exist for a specific field, like unique user IDs.
Does Keen MCP help me see what data I already have? +
Absolutely. You can use list_collections to see every stream of events, and list_saved_queries shows you all the existing reports available for reuse.
Is Keen MCP only for analyzing purchases? +
No. It's designed for any event data. You can use it to track anything from 'page views' to 'support ticket submissions', making it versatile for any app type.