Gacha Pity Simulator MCP for AI. Predict the true cost and chance behind random drops.
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Gacha Pity Simulator analyzes the math behind random loot box mechanics. Input base rates, soft pity triggers, and hard guarantees to predict expected pulls and estimate the true financial cost of rare items.
This tool gives you a clear statistical view of player spending risk.
What your AI can do
Estimate budgetary impact
Determines the overall financial cost, in currency or gems, needed to achieve a desired pull count.
Calculate pity metrics
Figures out how many pulls are expected to be needed by modeling the impact of various pity system rules.
Generate probability curve
Creates a graph that tracks your cumulative chance of success across every single pull attempt.
Determines how many draws are statistically needed to get an item, comparing results with and without pity guarantees.
Plots a graph showing the cumulative chance of success over time as you perform multiple pulls.
Calculates the total currency expenditure required to achieve a specific level of certainty or guarantee.
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Gacha Pity Simulator MCP with 3 Tools
These tools let you analyze gacha mechanics. Use them to calculate expected pull numbers, generate probability curves, or estimate the true financial cost of rare drops.
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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 Gacha Pity Simulator on VinkiusEstimate Budgetary Impact
Determines the overall financial cost, in currency or gems, needed to achieve a desired pull count.
Calculate Pity Metrics
Figures out how many pulls are expected to be needed by modeling the impact of...
Generate Probability Curve
Creates a graph that tracks your cumulative chance of success across every single...
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Claude AI
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Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
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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 3 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The biggest headache in game development is figuring out player spending expectations.
Right now, product managers have to run these calculations manually. They open spreadsheets, adjust drop rates by hand, and copy-paste numbers into separate risk models. If they change one variable—say, the soft pity trigger—they lose hours just recalculating everything from scratch, often introducing human error in the process.
With this MCP, you feed the base parameters once. The system handles the complex math for calculating expected pulls and visualizing the chance curve automatically. You get an instant statistical risk report, freeing up days of manual spreadsheet work.
Understand the true financial stakes with `estimate_budgetary_impact`.
You no longer have to guess at cost. Instead of relying on a 'high probability' estimate, you input your desired confidence level (like 95%) and the tool outputs the exact currency expenditure needed to guarantee that level of certainty.
This means every financial decision is backed by hard numbers. You know precisely what players are spending for an item at any given pity stage.
What your AI can actually do with this
The Gacha Pity Simulator is for anyone who needs to understand the genuine math behind random drop systems. It lets you simulate complex mechanics like base rarity rates, soft pity ramps, and hard guarantees. You can run calculations to find out how many pulls are statistically expected before a rare item drops, regardless of how confusing the game's stated probabilities are.
Furthermore, it generates probability curves showing exactly when your chances jump up—and calculates what that full process might cost in actual currency or internal gems. If you’re developing a game economy or just trying to understand the real risk of spending on a gacha title, this is the tool. You connect everything through Vinkius, accessing it alongside thousands of other specialized connectors.
019ee95f-511a-7163-8550-7423b9d9f86c 
Here's how it actually works
The bottom line is that this MCP takes complex game math and converts it into clear, actionable statistics about chance and money.
Input core mechanics: Define the item's base drop rate, the soft pity trigger point, and the hard pity limit.
Select the metric you need—whether it’s calculating expected pulls or estimating total cost—and run the simulation through the tool.
Review the generated probability curve and final numbers to see the statistical risk profile of the item.
Who is this actually for?
This connector is for people who deal with predictable systems built on unpredictable outcomes. Game economy designers need this when budgeting new mechanics; data analysts use it to model player spending habits; and serious players rely on it to understand the true cost of getting rare loot.
Uses calculate_pity_metrics to ensure a new monetization system hits retention goals without bankrupting the player base.
Runs simulations across different pity parameters using estimate_budgetary_impact to forecast revenue potential and risk exposure.
Generates a probability curve by simulating worst-case scenarios to stress-test the perceived fairness of the game's drop rates.
What Changes When You Connect
Instead of guessing, you can use calculate_pity_metrics to see if your pity system actually works. This tells developers exactly how much safer their economy is by guaranteeing certain pulls.
You get a clear picture of spending risk. Running the estimate_budgetary_impact tool shows whether chasing that rare item will cost 10% or 50% of a player's total currency supply.
Visualizing chance is easy with generate_probability_curve. You instantly see where your odds accelerate, pinpointing exactly when spending starts paying off.
It moves beyond simple average rates. By modeling soft and hard pity thresholds, the simulator provides a much more realistic view of player expenditure than basic math can offer.
You avoid making monetization decisions based on vague statements. You get concrete data that tells you the minimum required investment to feel certain about getting an item.
See it in action
Designing a new rarity system
A game designer wants to know if increasing the hard pity from 90 to 100 is worth the cost. They use calculate_pity_metrics and see that it significantly reduces expected pull counts, confirming the change improves player satisfaction metrics.
Analyzing a competitor's monetization
A data analyst is reviewing a rival game’s loot box system. They use generate_probability_curve to see that despite their stated 1% rate, the curve only hits 80% certainty by pull 75, making it artificially predatory.
Budgeting for a massive content drop
The product manager needs to calculate how much currency reserves are needed if they promise players a high chance of getting an item. They use estimate_budgetary_impact with the full probability dataset, budgeting $120,000 instead of guessing.
Testing a soft pity mechanism
A QA tester needs to validate if a new 70-pull soft pity system actually lowers costs. They run calculate_pity_metrics and compare the result against the old, higher expected pull count, confirming their fix works.
The honest tradeoffs
Assuming linear probability scaling
Thinking that a 50% chance of success means you only need two pulls. The actual variance and pity mechanics make the cost unpredictable.
Run generate_probability_curve to see how the curve accelerates. Use calculate_pity_metrics to get an accurate expected pull number, rather than just relying on simple percentages.
Ignoring financial consequences
Promising players a rare item without knowing the cost in currency units for achieving that high probability.
Always run estimate_budgetary_impact first. This forces you to calculate the actual dollar or gem count required, grounding your promises in real numbers.
Over-relying on base rates alone
Designing a system that only considers a 1% drop rate without factoring in any pity guarantees for player retention.
Use calculate_pity_metrics to model the full impact of your pity systems. This ensures you balance rarity with predictability, keeping players engaged.
When It Fits, When It Doesn't
Use this MCP if predicting resource cost and statistical risk is core to your task. You need a tool when 'how many pulls' or 'what is the budget for X chance' are critical variables. Don't use this if you just need simple arithmetic, like calculating total drops across 100 tries—a basic spreadsheet works fine then. If you only care about making sure the item can drop eventually, and not how fast or how much it costs, a general statistics package is enough. But when rarity rates interact with guaranteed safety nets (pity), this MCP is required because its tools are built specifically to model that interaction.
Questions you might have
What is the difference between expected pulls with and without pity? +
Without pity, the expected number of pulls is simply the inverse of the base rate. With pity, the calculate_pint_metrics tool accounts for the increased probability during the soft pity period and the 100% guarantee at hard pity, typically resulting in a much lower average pull count.
How can I estimate how much currency I need for a specific item? +
You can use the estimate_budgetary_impact tool. By providing your currency cost per pull and a probability curve generated by generate_probability_curve, you can see the average cost and the cost required to reach specific confidence levels like 90% or 99%.
Does this simulator support soft pity mechanics? +
Yes, the simulator is specifically designed to model soft pity. You can define a threshold where the rate begins to increase and specify the increment amount per pull.
What are the rate limits when using calculate_pity_metrics? +
The MCP supports high throughput for calculation requests. For users running large batch simulations, check Vinkius' service guidelines to ensure consistent performance and optimal usage.
If I input impossible rates, what should I expect from estimate_budgetary_impact? +
The tool returns a structured error message immediately. This response specifies exactly which parameter (pull chance or cost) is mathematically invalid and explains why the calculation failed.
Can generate_probability_curve model systems with non-linear pity mechanics? +
Yes, it handles complex probability drops accurately. Simply provide the soft/hard thresholds and increments; the function plots the cumulative chance across those specific points.
For accurate results, what data types does estimate_budgetary_impact require for cost inputs? +
It requires numerical values for pull costs and probability percentages. Using consistent units—like always defining costs in gems or dollars—is crucial for the output to be correct.
Is my simulation data secure when running calculations with calculate_pity_metrics? +
Yes, all input parameters and resulting metrics are confidential. Vinkius processes the data solely for the calculation and does not store any personal game account details.
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