# Analyze Your Accelerator Recruitment Funnel AI Agent Connect > Accelerator Selection Funnel Analyzer provides deep insights into your recruitment lifecycle. It lets your AI agent calculate stage conversion rates, pinpoint specific drop-off reasons, and suggest actionable process improvements. You can use the MCP to identify bottlenecks and optimize the selection flow. It also gives you a real-time overview of applicant quality and attrition risk. ## Overview - **Category:** business-intelligence - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_ABIkAEzxZHCi8nyeAGGHqW1Ysvff7g957HYQsjVC/ai-agent-connect - **Tags:** funnel, conversion, recruitment, optimization, analytics ## Description This MCP gives you deep analytical insight into the entire accelerator recruitment process. It lets your AI agent calculate how many candidates move from one stage to the next, pinpoint exactly where and why people are dropping out, and suggest concrete ways to improve your process. Instead of guessing where your pipeline is failing, you get data. You can get a high-level view of the current applicant pool's quality and velocity. Need to know why candidates are leaving? You can pinpoint specific drop-off reasons, optionally filtering by a specific cause. The MCP also calculates the percentage of candidates progressing through every stage, and you can even include quality weighting in those calculations. Finally, it suggests actionable improvements to the accelerator's process based on the funnel performance, helping you mitigate risks like timing lags. ## Tools ### get_candidate_health_report Provide a high-level overview of the quality and velocity of the current applicant pool ### identify_dropoff_patterns Optionally filter by a specific reason. Pinpoint exactly why and where candidates are leaving the funnel ### analyze_funnel_conversions You can optionally include quality weighting. Calculate the percentage of candidates progressing through every stage of the recruitment process ### detect_optimization_opportunities Suggest actionable improvements to the accelerator's process based on funnel performance ## Prompt Examples **Prompt:** ``` Calculate the conversion rates for my current application data. ``` **Response:** ``` The conversion rate from Application to Initial Screening is 45%, and from Initial Screening to Interview Invite is 30%. ``` **Prompt:** ``` Why are candidates dropping off after the interview stage? ``` **Response:** ``` The primary reason for drop-off after interviews is 'declined_offer', accounting for 15% of total losses. ``` **Prompt:** ``` Give me a health report for the current applicant pool. ``` **Response:** ``` The current pool has an average quality score of 82/100 and an average lead time of 5 days. The attrition risk level is low. ``` ## Capabilities ### Assess pool quality Use this when you need a quick, high-level overview of the current applicant pool's quality and speed. ### Find drop-off causes Use this to pinpoint the exact reasons and stages where candidates are exiting the funnel. ### Measure stage progression Use this to calculate the percentage of candidates moving through each stage, including quality weighting. ### Suggest process fixes Use this when you want the MCP to recommend concrete, actionable improvements to your selection process. ## Use Cases ### Improving Screening Efficiency Your team notices a high drop-off rate between the application and initial screening. You use the MCP to pinpoint if the issue is the application form itself or the screening criteria. ### Evaluating Program Changes After implementing a new interview stage, you use the MCP to measure the conversion rate through that specific step and compare it to historical data. ### Assessing Candidate Quality Before committing resources to a full cohort, you run a health report to see if the current applicant pool has sufficient average quality and velocity. ### Reducing Attrition Risk You use the MCP to detect optimization opportunities, helping you proactively adjust your process to mitigate risks like long timing lags. ## Benefits - You determine the exact stage where candidate interest drops, allowing you to fix the process, not just the candidates. - The MCP provides a real-time quality score for the applicant pool, helping you gauge immediate hiring risk. - You move beyond simple counts by calculating weighted conversion rates, factoring in candidate quality. - It turns raw funnel data into a list of concrete, actionable improvements for your team. ## How It Works Connecting this MCP to your AI client gives you immediate access to deep funnel analytics. You simply prompt your agent with a question, and the MCP executes the necessary calculations and reports. 1. Connect your preferred AI client (Claude, Cursor, Windsurf, VS Code) to the Vinkius catalog. 2. Prompt your agent with a specific question, like 'Analyze funnel conversions for Q3 data.' 3. The MCP runs the calculation, accessing candidate data and identifying bottlenecks. 4. Your agent delivers a clear, actionable report detailing conversion rates, drop-off reasons, and suggested improvements. ## Frequently Asked Questions **Does this MCP account for candidate quality?** Yes. The MCP allows you to calculate conversion rates while optionally including quality weighting. This means the analysis isn't just based on headcount, but on the quality of the candidates progressing through the funnel. **What kind of data does it analyze?** It analyzes the entire recruitment lifecycle, from initial application through to the final stages. It focuses on conversion rates, drop-off points, and the overall health of the applicant pool. **Can I find out why candidates are leaving?** Absolutely. You can use the MCP to pinpoint specific drop-off patterns. You can even filter the results by a known reason to narrow down the cause. **Is this tool only for tech accelerators?** No. While focused on accelerators, the underlying tools analyze any structured recruitment pipeline. You can use it for any process with distinct stages and measurable conversions.