North Star Metric Modeler MCP for AI. Turn vague goals into measurable growth simulations.
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North Star Metric Modeler takes your vague company goals and maps them into quantifiable action plans. It lets you break down a single metric—like Monthly Active Users—into structural components, allowing you to predict exactly how improvements in areas like Engagement or Retention will impact the final number.
What your AI can do
Set north star goal
Establishes your primary metric, defining both your current performance and the desired target milestone.
Simulate lever improvement
Predicts your overall metric's impact based on a specified percentage improvement within one of the defined pillars.
Calculate contribution distribution
Analyzes and reports which specific pillar is most responsible for closing the gap between current performance and goal.
Sets the primary metric and target milestone for strategic planning.
Defines how core business pillars influence the overall North Star Metric using weighted relationships.
Predicts changes in the final metric based on specific, simulated improvements to any pillar.
Analyzes current data to determine which pillars contribute the most to achieving a larger goal.
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North Star Metric Modeler: 4 Tools
These tools let you define strategic goals, structure internal relationships, run growth simulations, and analyze which business levers are most critical for achieving a target metric.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using North Star Metric Modeler on VinkiusSet North Star Goal
Establishes your primary metric, defining both your current performance and the desired target milestone.
Simulate Lever Improvement
Predicts your overall metric's impact based on a specified percentage improvement...
Calculate Contribution Distribution
Analyzes and reports which specific pillar is most responsible for closing the gap...
Configure Driver Tree
Structures how key pillars relate to the North Star Metric and assigns weights...
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Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
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Start a conversation
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Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
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- Deploy to edge with MCPFusion framework
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Make Your AI Do More
Start with North Star Metric Modeler, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 4 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The struggle of mapping strategic intent to daily tasks.
Today, figuring out your company's next move means running multiple reports. You check the Acquisition dashboard to see sign-ups. Then you switch to Product Analytics to check activation rates. Finally, you pull a Retention report to measure churn. You spend hours copying numbers into spreadsheets, trying to manually build a model that shows how all these separate metrics affect your overall success.
With this MCP, those manual steps vanish. You define the goal once, map out the structure using configure_driver_tree, and then let your agent run the entire analysis. You get an instant, weighted view of what matters most, so you can stop building models and start making changes.
Getting actionable growth insights with North Star Metric Modeler.
Instead of opening four different tabs—one for each pillar (Acquisition, Engagement, Retention)—you tell your agent to analyze the contribution. You run calculate_contribution_distribution and it immediately tells you that 'Engagement' is responsible for 40% of the required lift, while 'Monetization' only contributes 5%.
You don't just get a number; you get immediate resource prioritization. Your next meeting agenda isn't based on who yelled the loudest; it’s based on mathematical proof of where your effort should go.
What your AI can actually do with this
Figuring out how to grow is usually about guesswork until you build a model. This MCP helps bridge that gap by turning high-level objectives into weighted, measurable driver trees. You start by setting a clear target using your current performance data and defining what success looks like for your business.
From there, the system lets you map structural relationships among key pillars—things like Acquisition, Activation, or Monetization—and assign weights to them so they accurately reflect how they contribute to the North Star Metric. Once that framework is set, you can run simulations. You don't have to guess; you just tell it which pillar improved by a certain percentage, and it predicts your new overall metric value.
If you're stuck trying to figure out where to focus resources, this tool analyzes every component to show exactly which levers are most critical for closing the gap between where you are now and where you want to be. It’s the strategic planning engine that helps turn ambition into a clear, actionable roadmap; Vinkius hosts this MCP so your agent can access it directly from any compatible client.
019eeae5-351c-7323-8c33-9f14d7552868 
Here's how it actually works
The bottom line is you get a quantifiable plan showing exactly which internal changes will move the needle most effectively.
First, define your target by setting the core North Star Metric and desired end state.
Next, map out the structural relationships between key business pillars and assign weights to show their influence on that metric.
Finally, run a simulation or distribution analysis using your agent to predict outcomes based on hypothetical improvements.
Who is this actually for?
This MCP is for Growth Leads and Product Managers who are done with gut feeling. If your team spends more time arguing about priorities than building features, you need this. It forces concrete math onto vague strategic intent.
Uses the tool to validate feature priority by simulating how improvements in a specific area (like user activation) will boost the overall core metric.
Runs comparative analyses on different pillars—comparing Acquisition versus Engagement—to prove which growth channel deserves the next quarter's budget.
Sets the foundational, high-level North Star Goal and structures the entire business model to ensure all departments are working toward quantifiable outcomes.
What Changes When You Connect
Pinpoint resource gaps instead of guessing. Use calculate_contribution_distribution to see precisely which pillar is holding back your growth, eliminating wasted effort on low-impact areas.
Test strategies risk-free. Simulate a 20% boost in Retention using simulate_lever_improvement and instantly know the projected impact on your North Star Metric without building anything first.
Build structured strategy. Use configure_driver_tree to formally map how all parts of your business—Acquisition, Engagement, etc.—fit together into one cohesive growth model.
Set a clear destination. Start every project by defining a measurable objective using set_north_star_goal, giving your entire team a single source of truth for success metrics.
Accelerate decision-making. Instead of holding multiple meetings to debate priority, run the numbers and let calculate_contribution_distribution provide an data-driven answer.
See it in action
The Retention Strategy Meeting
A Product Manager needs to prove that investing in better onboarding flow is worth the cost. They use set_north_star_goal to define their target increase, configure_driver_tree to establish 'Activation' as a key pillar, and then run simulate_lever_improvement by boosting the Activation metric by 15%. The agent returns a projected lift of 8,000 users.
Budget Allocation Review
The Marketing Director needs to argue for more funding for Content creation. They run calculate_contribution_distribution against their goal and discover that 'Content' (part of Engagement) is responsible for 45% of the required growth, giving them hard evidence for increased spending.
Defining New Business Lines
A CSO wants to know if a new monetization pillar makes sense. They use configure_driver_tree to add 'Monetization' and assign a weight of 10%. Then, they run simulate_lever_improvement, showing that the combined lift is sufficient to meet their goal.
Post-Quarter Performance Review
After reviewing poor performance, an executive asks which area needs immediate attention. They use calculate_contribution_distribution against the existing structure and immediately see that 'Engagement' has a contribution of only 15%, signaling where all resources must shift.
The honest tradeoffs
Optimizing disconnected metrics
A team focuses solely on maximizing sign-ups (Acquisition) without considering if those users actually stick around. They treat each metric as a silo.
First, use set_north_star_goal to establish the ultimate goal. Then, configure_driver_tree to force all metrics to connect back to that single target. This makes sure every effort contributes to one outcome.
Running simulations without structure
A user runs simulate_lever_improvement but doesn't know which pillars are weighted correctly, leading to inaccurate predictions.
Always start by running configure_driver_tree. This step builds the mathematical relationship between your pillars and the North Star Metric, ensuring your simulations are based on a sound model.
Mistaking correlation for cause
A user sees that when Sales increases, users also increase, but they assume the two are directly linked without quantification.
Use calculate_contribution_distribution. This tool forces you to quantify which specific pillar is mathematically responsible for closing your gap, moving you past mere observation.
When It Fits, When It Doesn't
Use this MCP if your problem is: 'We know we need to grow X metric, but we don't know how or where to start.' It’s essential when you have multiple functional areas (Acquisition, Retention, etc.) that all contribute to one main goal. Don't use it if you just need a simple dashboard view of current numbers; those tools will work fine. You also don't need this if your problem is purely qualitative—if the solution requires legal review or emotional buy-in from stakeholders who can't be quantified. This MCP only handles quantifiable, multi-pillar growth strategy, so make sure you have the underlying data ready for calculation.
Questions you might have
How does set_north_star_goal work with other tools? +
set_north_star_goal defines the single, overarching target. Every subsequent tool—like configure_driver_tree or simulate_lever_improvement—uses this defined goal as its anchor point for all calculations.
Can I use calculate_contribution_distribution without first setting a North Star Goal? +
No. The distribution analysis must reference a target gap to be meaningful. You need set_north_star_goal to define the current state versus the desired end state before calculating contribution.
What if I change my core metric? Do I have to reconfigure everything? +
Yes. If you change your North Star Metric, you must first update it using set_north_star_goal and then rerun configure_driver_tree to correctly reflect the new structural dependencies.
What does simulate_lever_improvement calculate? +
It predicts the total impact on your North Star Metric if you increase a specific pillar (like Activation) by a certain percentage, based on the weights established in the driver tree.
If I use `configure_driver_tree` and my pillar weights don't sum to 100%, what happens? +
The MCP will throw a structural validation error. The tool requires the total weight of all defined pillars (Acquisition, Engagement, etc.) to exactly equal 100% for the model to run correctly.
When I call `set_north_star_goal`, do I need to provide historical performance data? +
No. You only input your current metric value and your desired target milestone. The MCP handles calculating the required delta based on those two figures.
Can `simulate_lever_improvement` accept both percentage increases and absolute user counts for pillar gains? +
Yes, it accepts both formats. You can provide a raw number or a percentage increase; the MCP scales the input correctly according to your configured driver weights.
If my North Star Metric already meets its target, how does `calculate_contribution_distribution` respond? +
It will run successfully and report that zero contribution is needed. It confirms you've closed the gap without assigning any required growth percentage to specific pillars.
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