# Retention Curve Analyzer MCP > Retention Curve Analyzer MCP. It analyzes user retention sequences to tell you if your product is actually sticking or if people are just dropping off. It checks your D1 to D90 metrics against real-world benchmarks for SaaS and gaming so you don't have to guess what good looks like. ## Overview - **Category:** product-management - **Price:** Free - **Tags:** retention, cohorts, churn, benchmarks, analytics ## Description You're looking at a spreadsheet of numbers and trying to figure out why users aren't coming back. This MCP takes that raw data and tells you the story behind the curve. Instead of staring at a wall of percentages, you can ask your agent to tell you if your retention is flattening out or if it's in a death spiral. It handles the heavy lifting of pattern recognition, identifying those smiling curves that show long-term health. You can also see how you stack up against the rest of the industry. If you're running a mobile game or a SaaS platform, it pulls in the standard benchmarks for those specific markets to see if your D7 or D30 numbers are actually competitive. It turns a manual research project into a quick conversation with your AI client. Since it's hosted on the Vinkius catalog, you can plug it into your existing workflow and get these insights without building a custom dashboard. It's about moving from I think we're doing okay to we are hitting the D90 target for our category. ## Tools ### analyze_retention_trend Tells you if your retention sequence is flattening or dropping. ### classify_curve_pattern Labels the specific shape of your user retention curve. ### verify_benchmark_compliance Checks your D1, D7, and D90 metrics against industry standards. ## Prompt Examples **Prompt:** ``` Is my retention curve flattening or declining? Here are the values: [100, 50, 30, 25, 22] ``` **Response:** ``` The retention curve is Flattening. The recent drop from 25 to 22 is minimal, indicating the curve has reached a plateau. ``` **Prompt:** ``` Check if my SaaS product meets D7 benchmarks. My D1 is 40% and D7 is 20%. ``` **Response:** ``` The compliance report shows that your D7 metric failed to meet the SaaS benchmark. ``` **Prompt:** ``` What pattern does this sequence represent: [100, 80, 60, 40, 20, 5]? ``` **Response:** ``` The pattern is classified as Declining because the sequence shows a continuous downward trend approaching zero. ``` ## Capabilities ### Identify if retention is leveling off Determine if your user sequence is reaching a stable plateau or continuing to drop. ### Spot declining trends early Catch high churn risk patterns before they impact your long-term growth. ### Compare metrics against industry standards See how your D1 to D90 numbers stack up against SaaS, gaming, or e-commerce benchmarks. ### Detect smiling curves Confirm if your product has reached a healthy, stable state of long-term retention. ### Automate benchmark compliance Get a clear pass or fail report on your cohort metrics compared to market averages. ## Use Cases ### Validating a new onboarding flow A PM wants to know if a new UI update helped. They give the agent retention numbers and use analyze_retention_trend to see if the curve is leveling off. ### Identifying churn risk in new cohorts A growth lead is worried about a new cohort. They use classify_curve_pattern to see if the drop-off is a standard declining curve or something unique. ### Reporting to stakeholders An analyst needs to report to the board. They use verify_benchmark_compliance to show that their D7 numbers beat the industry average for e-commerce. ### Confirming mobile game stickiness A game dev wants to know if they've hit sticky status. They ask the agent to find the smiling curve in their latest mobile gaming data. ## Benefits - Get immediate clarity on whether your retention is flattening out or continuing to drop using analyze_retention_trend. - Stop wasting time on manual math and see your curve shape instantly with classify_curve_pattern. - Know exactly where you stand in the market by checking your D30 and D90 numbers against industry standards with verify_benchmark_compliance. - Spot smiling curves early to confirm your product has reached a healthy, stable state. - Save hours of research by getting automated SaaS and mobile gaming benchmarks for every cohort. - Make data-backed decisions in meetings by showing your agent's analysis of your churn risk. ## How It Works The bottom line is you get an objective look at your product's stickiness without the manual math. 1. Feed your cohort retention numbers into your AI client. 2. The MCP processes the sequence to find the underlying pattern. 3. You get a clear status on whether you're hitting benchmarks or losing users. ## Frequently Asked Questions **How does Retention Curve Analyzer help with churn?** It identifies Declining Curves that indicate high churn risk so you can intervene before the cohort dies. **Can Retention Curve Analyzer check my D30 metrics?** Yes, use verify_benchmark_compliance to see how your D30 numbers stack up against SaaS, gaming, or e-commerce standards. **What is a smiling curve in Retention Curve Analyzer?** It's a pattern where retention stabilizes at a healthy level, which you can identify using classify_curve_pattern. **Does Retention Curve Analyzer work for mobile games?** Yes, it includes specific industry benchmarks for the mobile gaming category. **How do I know if my retention is flattening?** You can use analyze_retention_trend to get a definitive answer on whether your sequence is reaching a plateau. **What clients are compatible with the Retention Curve Analyzer?** It works with any MCP-compatible client, including Claude, Cursor, and Windsurf. You just need to connect it through Vinkius to start using the tools. **How does Retention Curve Analyzer handle my sensitive cohort data?** It doesn't store or save your numbers. The MCP just processes the data you input to run the analysis and then moves on to the next request. **Can I use verify_benchmark_compliance for industries other than SaaS, Mobile Gaming, and E-commerce?** Not currently. The tool uses hardcoded standards for those three specific categories. For other industries, you'll need to provide your own target metrics.