Mixpanel MCP. Audit user funnels and track retention from natural conversation.
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Mixpanel (Event Analytics & Insights) connects product analytics to your agent. Query event trends, calculate conversion funnels, and audit user cohorts using natural conversation.
This server lets you analyze raw user behavior data—from session starts to specific button clicks—without building custom reports.
What your AI agents can do
Export events
Exports raw, atomic data logs for all events that occurred in the project (rate-limited to 60/hour).
List cohorts
Lists every saved behavioral user cohort configured within your Mixpanel project.
List funnels
Retrieves a list of all saved funnel configurations you can analyze for drop-offs.
Search and retrieve detailed company metadata or traits for individual users using JQL expressions.
Compute the decay rate of a user base over time, identifying how long different cohorts remain active.
Get step-by-step data on funnel performance and pinpoint exactly where users are dropping off in their journey.
Filter any event stream by specific attributes, like device type or geographical region.
Retrieve a list and size count of saved behavioral cohorts to gauge the scale of different user types.
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Mixpanel (Event Analytics & Insights) Server: 10 Tools
Use these ten dedicated tools to query event trends, list cohorts, and compute conversion data directly through your AI agent.
019d75d5export events
Exports raw, atomic data logs for all events that occurred in the project (rate-limited to 60/hour).
019d75d5list cohorts
Lists every saved behavioral user cohort configured within your Mixpanel project.
019d75d5list funnels
Retrieves a list of all saved funnel configurations you can analyze for drop-offs.
019d75d5query events
Queries the total count and trends of specific Mixpanel events over defined time periods.
019d75d5query funnel
Calculates conversion data for a specified, saved funnel configuration.
019d75d5query insights
Runs complex, pre-built dashboard insights and reports against your user data.
019d75d5query profiles
Searches for specific user profiles using advanced JQL query language to pull detailed metadata.
019d75d5query retention
Computes the retention curve, showing what percentage of users return over time for a given cohort.
019d75d5query segmentation
Breaks down an event's metrics by filtering on specific user properties (like device or region).
019d75d5query top events
Gets a list of the top 20 events that have occurred most frequently in your project.
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What you can do with this MCP connector
Mixpanel Analytics MCP Server lets you talk directly to your product data. You ditch the clunky dashboards and just ask your agent what you need, getting deep user insights on the fly. This server gives you full access to tracking raw user behavior—from when they first land until they click that final button.
It’s designed so you don't have to build a custom report; you just talk to the data.
How Mixpanel MCP Works
- 1 Subscribe to the server, then provide your Mixpanel Service Account credentials (User/Secret) and Project ID.
- 2 Your AI client connects to the endpoint. You prompt it with a request—for instance, 'Show me the retention curve for Pro users.'
- 3 The agent executes the necessary tool call (e.g.,
query_retention), gets the data back, and presents the calculated insight in plain text.
The bottom line is: you talk to your product analytics data instead of building a dashboard.
Who Is Mixpanel MCP For?
Product Managers who are tired of manually compiling weekly feature adoption reports. Data Scientists who need raw, segmented event logs for modeling. Growth Leads needing constant visibility into funnel drop-offs to optimize user acquisition.
Audits feature adoption rates and monitors conversion funnels by asking the agent directly, eliminating manual report generation.
Tracks retention trends and experiments with property-based segmentation to optimize user acquisition without leaving their workspace.
Retrieves raw event logs (export_events) and queries detailed user profiles via JQL for rapid diagnostic investigations.
What Changes When You Connect
- Analyze granular data using
query_segmentation. Instead of looking at total clicks, you can split the data to see only clicks from 'Pro' plan users in the EU region. It’s powerful filtering on demand. - Determine product stickiness with
query_retention. You run a query comparing cohorts; it spits out the Day 7 and Day 30 retention rates immediately, telling you if your onboarding flow is actually working long-term. - Find bottlenecks using
list_funnelspaired withquery_funnel. If conversion drops off between Step 2 and Step 3, the agent tells you exactly which step has the highest drop rate. No guesswork involved. - Audit user groups with
list_cohorts. Need to know how many 'Power Users' exist right now? Just ask for the cohort size; it pulls the number instantly so you can prioritize feature development. - Retrieve raw data with
export_events. If a dashboard is missing key fidelity, this tool lets you grab all the original event logs for deep investigation or external modeling.
Real-World Use Cases
Investigating a drop in 'Checkout Success'
The Growth Lead notices checkout success dipped last week. They prompt their agent: 'Compare Funnel X's conversion rate to the previous month, but only for iOS users.' The agent runs list_funnels and query_segmentation, pinpointing that the drop is exclusively due to a change in iPhone OS version 17.
Measuring long-term 'Product Stickiness'
A PM wants to know if their new onboarding flow actually makes users stick around. They ask the agent to run query_retention on the cohort that signed up last quarter. The result tells them, in percentages, how many of those users are still active 60 days later.
Finding high-value user segments
The Data Analyst needs to know the size and characteristics of their most engaged users. They ask the agent to list_cohorts and then follow up with a profile query, instantly getting metadata for the 'Power Users' cohort.
Debugging missing dashboard data
The team suspects Mixpanel is undercounting specific actions. Instead of trusting the dashboard, they use export_events to grab raw logs and manually verify if the expected events are present for a given date range.
The Tradeoffs
Querying data in silos
Just running query_top_events tells you what happened, but not who did it or if those events relate to the current funnel bottleneck.
→
Don't just run a top event query. Combine tools: First, use list_funnels to identify the drop-off step, then use query_segmentation on that specific funnel step to see which device types are failing.
Ignoring user context
Running a general query like 'How many users signed up?' gives you volume but no actionable insight into why they might churn.
→
Always start by segmenting. Use query_segmentation to filter the initial event stream by key properties (e.g., plan type or region) before querying any metric.
Over-relying on pre-built views
Assuming a dashboard's 'Active Users' count is accurate without checking for data gaps or segment drift.
→
Verify the source. If the numbers seem off, run query_profiles with JQL to audit the user metadata and confirm what the system actually sees as an active user.
When It Fits, When It Doesn't
Use this server if your core problem is understanding why users are doing (or not doing) things in your product. You need cross-sectional analysis: mapping a journey (list_funnels), measuring persistence (query_retention), and isolating specific groups (query_segmentation).
Don't use this if you just need to count how many times button X was clicked—that's basic event tracking, which the tools handle. More importantly: don't assume one tool answers everything. If you only run list_cohorts, you'll know who is in a segment, but you won't know what they did next. You must combine calls; e.g., find the cohort using list_cohorts > check their events via query_events > filter that data by region using query_segmentation. This combination of tools is where the real value lies.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Mixpanel. 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
Manually piecing together user flow from different tabs used to take hours.
Today, finding out why users drop off requires jumping between the 'Funnels' tab, then cross-referencing that data with a separate 'User Segmentation' report, and finally exporting raw event logs just to verify the counts. It’s copy-paste hell, and you always lose context somewhere in the spreadsheets.
With this MCP server, you simply ask: 'Show me where users leave during checkout.' The agent runs the necessary `list_funnels` and `query_segmentation` calls, giving you a single, calculated answer without needing to touch a report builder.
Mixing up event data with `export_events`.
The common mistake is trying to use raw logs for high-level metrics. You pull hundreds of thousands of rows via `export_events` and then try to manually count the unique user journeys—a task that's impossible at scale.
Instead, let your agent run a targeted query like `query_top_events`. It handles the massive data processing for you, returning only the actionable metrics you need. The complexity is hidden from you.
Common Questions About Mixpanel MCP
How do I check retention using the `query_retention` tool? +
You must specify a starting event and an ending point (e.g., 'User Signup' to 'App Open') and a date range. The agent computes the curve, showing you day-by-day drop-off percentages.
What is the difference between `query_events` and `export_events`? +
query_events gives you aggregated counts (e.g., '50,000 Button Clicks'). export_events pulls all the raw data logs for those events—useful only when you need to analyze individual records.
Can I segment my funnel analysis? +
Yes. You use list_funnels first to identify the funnel, and then combine that with query_segmentation to filter the conversion data by properties like 'Pro' plan users.
How do I find out which user groups are most valuable? +
You run list_cohorts to see all saved segments. Then, you use that cohort name in a profile query (query_profiles) or retention query (query_retention) to determine their value.
What are the rate limits when I use the `export_events` tool? +
The API is limited to 60 requests every hour. If you run into a limit, wait for the hourly window to reset and try again. For large-scale data extraction, consider batching your reports manually.
How do I authenticate my account before using `query_insights`? +
You must provide your Mixpanel Service Account (User/Secret) and Project ID during setup. Your AI client uses these credentials to connect securely and run the query.
What format does `query_profiles` expect for user filtering? +
It requires proprietary JQL expression variables, like g., properties["plan"]=="pro". The AI will generate this complex syntax based on your natural language request.
How can I see all my existing funnels using the `list_funnels` tool? +
The list_funnels tool retrieves a list of every saved funnel configuration in your project. You then use that name to pass into query_funnel for analysis.
Can I query my conversion funnels through my agent? +
Yes. Use the query_funnel tool with a specific Funnel ID and date range. Your agent will retrieve the step-by-step conversion percentages and drop-off counts, allowing you to identify exactly where users are leaving your flow.
How do I filter user profiles using specific properties through a conversation? +
The query_profiles tool allows your agent to execute JQL expressions. You can ask your agent to find users matching literal conditions like properties['plan'] == 'pro', and it will retrieve the corresponding profiles and metadata securely.
Can my agent check user retention for my application? +
Absolutely. Use the query_retention tool by providing a 'born' event (first action) and a recurring 'return' event. Your agent will compute the N-Day retention curve, showing you how sticky your product is for specific user cohorts.
Multi-server workflows that include Mixpanel (Event Analytics & Insights) MCP
Get Product Intel Before Sales Demos via MCP
Your AE is about to demo the dashboard feature for 30 minutes to a prospect who already used it 47 times in the trial , meanwhile the prospect only opened the API docs once and clearly needs help there
MCP Recipe to Optimize Client Conversion Rates
Your agency redesigned the client's homepage 3 weeks ago and everybody celebrated , but nobody checked Mixpanel to see that the new hero section reduced signups by 22% because the CTA button blends into the background on mobile
MCP Servers That Track Newsletter Conversions
Your newsletter has 12,000 subscribers and your product has 2,400 users but you have no idea how many of those users came from the newsletter , marketing says it is working and product says prove it
Segment Emails by User Behavior Using MCP
You send the same onboarding email to a power user who completed setup in 10 minutes and a user who has not logged in since signup , and you wonder why engagement is flat
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