# Founder Vision Prover MCP for AI Agents AI Agent Connect > Founder Vision Prover validates startup ideas by forcing your AI client to move past '1% of a $10B market' fluff. It audits your behavioral pain, bottom-up TAM, retention physics, distribution moats, and unit economics. It treats your pitch like a YC partner would, rejecting ideas that rely on paid ads or lack structural growth loops. ## Overview - **Category:** data-analysis - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_Zxns8LB04gZeu8P1t7bv48WSbtX3KQZsz2pAAzEL/ai-agent-connect - **Tags:** startup, venture-capital, y-combinator, founder, unit-economics, retention, distribution ## Description Founder Vision Prover is an Connector that turns your AI client into a ruthless venture partner. Most people ask an AI to evaluate a startup idea and get a polite 'this looks great' followed by a suggestion to run Facebook ads. That is not a strategy. It is a recipe for burning cash. This tool changes that by forcing the model to prove your idea actually works using five specific decision pivots. It demands present-tense evidence of customer pain, requires a bottom-up calculation of your reachable market, and audits your retention metrics against hard thresholds. It won't let you off the hook for distribution. It rejects 'run ads' and looks for structural moats like network effects or virality. If your unit economics don't show a CAC payback in under 12 months, it will flag your model as zombie economics. By using this Connector on the Vinkius marketplace, you move from hoping a business works to pressure-testing the underlying physics of your startup before you spend a dime. ## Tools ### validate_founder_vision This tool forces your AI client to audit a startup idea against five core pillars of venture-scale business physics. It identifies specific flaws like top-down market delusions or lack of structural distribution moats. ## Prompt Examples **Prompt:** ``` I want to build a travel app. I'll use Google Ads to get users. ``` **Response:** ``` ### 🚩 DISTRIBUTION_NAIVE "Google Ads" is a linear tax on growth. It scales with your spend and decays with competition. To be venture-scale, you need a structural moat. **Next Steps:** * Define a **$0 CAC loop** (e.g., network effects, virality, or product-led growth). * Explain how each user brings the next user without additional spend. * If you can't define a structural loop, this is a lifestyle business, not a scale-up. ``` **Prompt:** ``` Restaurant managers pay an intern $1k/month to update inventory. We charge $200/month. 15,000 restaurants x $2,400/year = $36M TAM. M3 retention is 85%. Each restaurant invites suppliers (network effect). CAC is $300, payback is 1.5 months. ``` **Response:** ``` ### ✅ VISION_PROVEN **Founder Vision Validated.** * **Behavioral Pain:** Intern hack exists ($1k/month waste). * **Bottom-up TAM:** 15,000 x $2,400 = $36M. * **Retention:** 85% M3 (Above 80% SaaS threshold). * **Distribution:** Supplier network effect drives CAC toward $0. * **Unit Economics:** 1.5-month payback, crossover at month 14. **Verdict: Execute.** ``` **Prompt:** ``` Users are annoyed they have to use two apps to book flights. We have 50,000 downloads. ``` **Response:** ``` ### 🚩 BEHAVIORAL_VOID "Annoyed" is a feeling, not evidence of a venture-scale pain. **The Problem:** * Customers are not currently **hacking a solution** (e.g., paying interns or duct-taping spreadsheets) to solve this today. * 50,000 downloads are a vanity metric. They prove interest, not retention. **Action Required:** * Show me the **present-tense hack**. Who is doing what today to survive this pain? * Provide **Month 3 cohort retention** data. ``` ## Capabilities ### Audit customer pain It forces the AI to find specific evidence of people hacking their own solutions today. ### Calculate bottom-up TAM It replaces 'percentage of market' guesses with actual customer counts multiplied by price. ### Verify retention physics It checks if your M3 cohort retention and margins meet venture-scale thresholds. ### Identify distribution moats It filters out paid ad plans to find structural, $0 CAC growth loops. ### Model unit economics It calculates your CAC payback timeline and crossover month to ensure capital cycles correctly. ## Use Cases ### Vetting a new SaaS idea A founder asks their agent to check a 'better CRM' idea. The tool catches the top-down market delusion and forces them to define a specific customer count. ### Auditing a pitch deck A VC uses the tool to see if a startup's 'Facebook Ads' plan is actually a moat or just a linear tax on growth. ### Validating a pivot A team wants to move from B2C to B2B. They use validate_founder_vision to see if the new B2B unit economics actually cycle capital fast enough. ### Checking growth loops An entrepreneur wants to see if their new app has a structural viral loop. The tool flags it if they can't describe a $0 CAC mechanism. ## Benefits - Stop wasting time on 'vitamin' ideas by forcing the AI to find evidence of actual customer pain using validate_founder_vision. - Avoid the 'top-down' trap by requiring the AI to calculate reachable customer counts for a real bottom-up TAM. - Identify 'leaky bucket' problems early by auditing M3 cohort retention and gross margins. - Build a sustainable business by rejecting paid ad plans in favor of structural $0 CAC distribution moats. - Ensure venture-scale growth by modeling CAC payback timelines and crossover months to avoid zombie economics. ## How It Works The bottom line is you get a brutal, data-backed reality check on your startup's viability instead of generic praise. 1. Input your startup idea or pitch deck into your AI client. 2. The Connector triggers a structured reflection that forces the model to address all 5 decision pivots. 3. You get a verdict matrix that flags specific physics violations or confirms the vision is proven. ## Frequently Asked Questions **Can Founder Vision Prover help me see if my startup is viable?** Yes, it does this by auditing five core pillars of business physics. It moves beyond surface-level praise to check your actual unit economics and growth moats. **Does Founder Vision Prover work for B2B and B2C ideas?** It works for both. It uses specific thresholds, like 80% retention for SaaS, to ensure your specific business model meets venture-scale standards. **How does Founder Vision Prover handle market sizing?** It forces you to provide a bottom-up calculation. It rejects '1% of a market' statements in favor of actual reachable customer counts multiplied by price. **Will Founder Vision Prover tell me how to run ads?** No, it actually pushes back on that. It looks for structural distribution moats like network effects or virality because paid ads are often a linear tax on growth. **Can I use Founder Vision Prover to check my unit economics?** Yes, it specifically models your CAC payback timeline. It ensures your capital cycles fast enough to reach venture scale without becoming a zombie business. **Is Founder Vision Prover good for early-stage founders?** It's built for early-stage founders who need a reality check. It identifies behavioral voids where customers aren't actually hacking a solution today. **Does it predict if my startup will succeed?** No. It validates STARTUP PHYSICS — the structural mechanics that make venture-scale growth possible. If your cohort retention leaks, your CAC payback is too slow, or your distribution is paid ads, no amount of vision will save the business. This tool stops the AI from flattering your bad ideas. **Why does it reject 'downloads' and 'signups' as proof of retention?** Downloads, signups, and waitlist size are vanity metrics. They measure interest, not retention. The only metric that proves retention is COHORT data: of users who joined in Month 1, what percentage are STILL ACTIVE in Month 3? If you cannot answer that, you have a leaky bucket. **What is a 'Behavioral Hack'?** If a problem is truly painful, the customer is already solving it TODAY using a hack — spreadsheets, interns, duct tape, custom scripts. They are spending money or time on an absurd workaround. If they are NOT hacking a solution, the pain is not real enough to justify a purchase.