# Analyze Startup Cohort Viability. AI Agent Connect > Accelerator Post-Program Survival Analyzer provides specialized tools to evaluate the post-graduation viability of startup cohorts. It calculates survival rates over time, identifies failure modes like 'early death' versus 'mature failure', and adjusts raw survival data based on market conditions and funding environments. Use the MCP to get high-level health scores, understand shutdown timing, and account for external economic factors. ## Overview - **Category:** finance - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_o6jK29TPbOYLItprazCxCGNxVkVrpEQh4mOQlRJs/ai-agent-connect - **Tags:** startup, survival-analysis, accelerator, metrics, economics ## Description This MCP gives you specialized analytical tools for evaluating how well startup cohorts fare after an accelerator program. Instead of just looking at raw numbers, you can calculate survival rates over time and pinpoint specific failure modes. It doesn't just give you a score; it adjusts that score based on current market conditions and funding environments. You can understand if a cohort's performance was strong because of its own merit, or if it benefited from a temporary economic boost. This MCP helps you move beyond simple metrics to truly understand the underlying health and risk of a group of companies. ## Tools ### get_survival_metrics Provides a high-level overview of the cohort's survival health over time ### get_environmental_adjustment Adjusts the observed survival data to account for external economic factors ### get_failure_analysis Identifies the specific ways and when companies in the cohort are failing ## Prompt Examples **Prompt:** ``` Calculate the survival health for a cohort of 50 companies where 40 are active after 12 months and 30 are active after 24 months, with a 10% pivot rate. ``` **Response:** ``` The cohort has a 12-month survival rate of 80% and a 24-month survival rate of 60%. The median survival time is calculated based on these metrics, and the health score reflects the impact of the 10% pivot rate. ``` **Prompt:** ``` Analyze the failure patterns for 50 graduated companies where 5 failed in month 6 and 10 failed in month 18, with a 5% pivot rate. ``` **Response:** ``` The analysis shows an early death rate of 10% (5 companies) and a mature failure rate of 20% (10 companies). The pivot impact score is calculated based on the 5% pivot rate. ``` **Prompt:** ``` Adjust a survival rate of 70% given a market condition index of 0.8 and a funding environment index of 0.9. ``` **Response:** ``` The adjusted survival rate is higher than 70% because the low market and funding indices indicate that the companies performed better than the difficult economic environment would suggest. ``` ## Capabilities ### Calculate survival rates The AI uses this MCP to determine the percentage of a cohort still active at various time points. ### Identify failure patterns It analyzes the timing and nature of company failures, separating early vs. mature declines. ### Adjust for macro factors The MCP adjusts raw survival data using external economic indices to provide a more accurate assessment. ### Determine median survival The analysis calculates the median survival time, giving a central measure of cohort longevity. ## Use Cases ### Due Diligence Review When evaluating a potential investment, you can run the MCP to see if the target cohort's survival rates are sustainable given current economic headwinds. ### Portfolio Health Check Run the analysis quarterly to check if the overall group of portfolio companies is showing signs of systemic failure or if the decline is isolated. ### Program Improvement After an accelerator class ends, use this MCP to analyze failure patterns and pinpoint which stage or type of company needs more support. ### Market Trend Analysis Compare the survival metrics of different industry cohorts (e.g., SaaS vs. HealthTech) to see which sectors are more resilient during downturns. ## Benefits - It calculates survival rates over time, giving you a clear picture of cohort attrition. - It separates failure modes, allowing you to distinguish between early-stage operational issues and later-stage market saturation. - It adjusts raw data using external economic indices, removing the noise of temporary market fluctuations. - It provides a high-level health score that accounts for both internal performance and external economic pressure. ## How It Works Connect your AI client to this MCP, input your cohort data, and specify the external economic factors you want analyzed. The MCP processes the data, calculates the adjusted metrics, and returns a detailed viability report. 1. Connect your AI client to the Vinkius catalog and select this MCP. 2. Provide the MCP with the raw cohort data and the time metrics (e.g., 12 months, 24 months). 3. Specify the external economic indices (market condition and funding environment). 4. The MCP runs the survival analysis and returns the adjusted health scores and failure breakdown. ## Frequently Asked Questions **What kind of data does this MCP need?** You need data on the cohort's activity over time, including how many companies are active at specific milestones. You also need external indices for market conditions and funding environments to run the full adjustment. **Can I tell the difference between early and late failure?** Yes. The MCP uses failure analysis to categorize shutdowns. It separates 'early death' from 'mature failure,' giving you insight into whether the problem was operational or market-related. **Is this just a simple metric calculator?** No. It goes beyond simple metrics. It uses survival analysis models to calculate adjusted health scores, meaning the results account for external economic factors that might skew raw data. **Does it predict the future?** It analyzes historical performance to calculate survival rates and median survival time. While it informs future planning, it is based on the data you provide and current economic models.