MCP Recipe for Product Kill or Ship Decisions.
Feature bloat eliminated, opportunity cost quantified, bold choice validated , decide whether to kill it or ship it with mathematical proof
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent receives a product pitch: 'We are building an AI-powered project management tool with real-time collaboration, Gantt charts, resource allocation, time tracking, AI sprint planning, Slack integration, Jira import, and a customizable dashboard.' Phase 1: the agent runs `validate_steve_jobs_vision`.
Radical Simplification: 8 features listed, zero killed. The agent demands subtraction , name the features you cut. Experience Backwards: the pitch says 'AI-powered project management tool.' That is tech-first, not human-first.
The agent forces: 'When a team lead opens it Monday morning, what do they FEEL? What happens in the first 3 seconds?' The rewrite: 'You open it.
Your week is already planned. No clicks needed.' System Autonomy: the AI plans the sprint without user configuration , no settings page, no preference menus.
Bold Choice: removing Gantt charts and time tracking. This will anger 40% of project managers who expect them. That polarization is the product's identity.
Ownership: the sprint planning AI must be built in-house, not a GPT wrapper. Verdict: kill 5 features (Gantt, time tracking, resource allocation, Jira import, customizable dashboard).
Keep 3 (AI sprint planning, real-time collaboration, Slack notification). Phase 2: the agent runs `validate_opportunity_cost` for each killed feature. Gantt Charts , Chosen Path: no Gantt.
Alternative: build Gantt. Cost of killing: 40% of enterprise prospects expect Gantt charts (source: G2 'must-have features' analysis). Opportunity Loss: estimated $200K ARR from enterprise segment.
Irreversible: if you build a reputation as 'the simple tool,' adding Gantt later contradicts the brand. Math: the simplification increases free-to-paid conversion by 15% (tested in prototype) = $350K additional ARR.
Gains ($350K) > Costs ($200K) + Irreversibility Risk ($50K estimated brand confusion). Verdict: COST_PROVEN , killing Gantt charts is mathematically justified.
The agent repeats this for each killed feature. Final output: a product vision that is radically simple AND economically validated.
Every killed feature has a dollar value assigned to the loss, and the math proves the gains exceed the sacrifice.
MCP Server Orchestration: 2 MCP Servers, one intelligent agent
Connect Steve Jobs Vision Prover and Opportunity Cost Prover MCP servers so your AI agent runs your product concept through radical simplification first, then quantifies exactly what you sacrifice by choosing your path over the discarded alternative. Product teams get a two-phase decision engine: first, the agent forces you to distill your product to a single core experience, kill features, and prove end-to-end ownership without tech jargon. Then, it quantifies the opportunity cost of every killed feature and every bold choice, mapping the dollar value of what you gave up, identifying irreversible tradeoffs, and proving that your simplification gains outweigh the combined losses. The result is a ship-or-kill verdict backed by both design rigor and economic proof.
Steve Jobs Vision Prover
triggerForces radical simplification, human experience focus, and end-to-end ownership validation
validate_steve_jobs_vision Opportunity Cost Prover
actionQuantifies the economic value of killed features and proves the math of simplification
validate_opportunity_cost Run This Automation Today
Connect Claude, ChatGPT, Cursor, or any AI agent to the Vinkius catalog and run this automation in minutes.
Build Your Own MCP
Turn any internal API into an MCP server. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Connect & Automate
The 2 servers this recipe uses are ready in the catalog. Connect them once, paste a prompt, and your AI runs the full workflow.
- Steve Jobs Vision Prover & Opportunity Cost Prover ready in the catalog right now
- Add more from 4,700+ servers whenever you need
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added every week
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 4,700+ MCP servers and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across every tool, in one conversation. That's what this infrastructure was built for.
Cross-Platform Intelligence
Your agent doesn't just connect to tools. It understands the relationships between them. Data flows where it needs to go, automatically, with full context preserved across every platform.
Contextual Reasoning
Every decision your agent makes considers the full picture. It reads CRM data, checks calendars, reviews conversation history, and acts on everything at once. Not step by step. All at once.
Productivity at Scale
What used to take 45 minutes across five different dashboards now takes one sentence. Your agent runs the entire workflow end to end while you focus on decisions that actually matter.
Zero-Config Reliability
No API keys to paste. No webhooks to configure. No YAML to debug. Connect your MCP servers once, and your agent handles the rest. Every time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius MCP catalog to handle these for you. You describe what you need. It does the rest.
Product managers deciding which features to cut from an MVP who need mathematical proof that the simplification gains outweigh the market opportunity lost
Founders preparing product strategy presentations for investors who need to justify bold design choices with economic validation rather than design philosophy alone
Design leads advocating for radical simplification against feature-request pressure who need dollar-value ammunition to defend the product vision
Product teams evaluating competitive feature parity who need to decide whether matching a competitor's feature set is worth the complexity cost or if differentiation through simplicity is mathematically superior
Frequently Asked Questions About This MCP Server Orchestration
Which MCP servers do I need for this workflow?
Two: Steve Jobs Vision Prover and Opportunity Cost Prover. Connect both to your AI client.
Does this work with Claude Desktop, Cursor or Windsurf?
Yes. Any AI client that supports the Model Context Protocol works , Claude Desktop, Cursor, Windsurf, Cline and others.
Is this only for product managers?
No. Any role making build-vs-kill decisions benefits: founders validating MVPs, design leads defending simplification, and CTOs evaluating feature scope against engineering cost.
What if the Opportunity Cost Prover disproves a feature kill?
That is the point. If the math shows that killing a feature loses more than you gain, the feature stays. The workflow does not bias toward simplification , it biases toward mathematical proof. Sometimes the bold choice is wrong.
Can this replace traditional product discovery?
No. This workflow validates decisions, it does not discover user needs. Use it after customer interviews, prototyping, and market research to validate whether your simplification strategy is economically sound.
How does it handle features with no clear revenue attribution?
The Opportunity Cost Prover requires you to estimate the value. If you cannot assign even an approximate dollar figure to a feature, it flags the decision as incomplete , you cannot prove the math if you cannot quantify the loss.
MCP servers used in this workflow
Steve Jobs Vision Prover
Steve Jobs Vision Prover runs your product idea through a rigorous, five-point validation process based on historical design failures. It forces you to define the single core experience, quantify what you killed, and prove end-to-end ownership without using tech jargon. This server rejects pitches that suffer from feature bloat or committee design.
Opportunity Cost Prover
Opportunity Cost Prover forces your AI agent to stop making simple decisions based on convenience. This engine runs a 6-pivot trap, forcing it to map direct costs, quantify lost opportunities from discarded alternatives, identify irreversible tradeoffs (like vendor lock-in), and prove the math before giving you an answer.