MCP For Claude Marketplace
Gacha Pity Probability Calculator

Gacha Pity Probability Calculator MCP Connector for Claude

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Simulate gacha pity mechanics and analyze monetization impact.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

A deterministic modeling engine for game designers to simulate gacha monetization and player spending behavior. Use calculate_pity_dynamics to model step-by-step probability mechanics, including base rates, soft pity ramps, and hard pity guarantees. Once the dynamics are modeled, use analyze_monetization_impact to calculate expected costs and identify design risks like predatory mechanics. You can also use get_pity_summary to quickly extract statistical central tendencies like median pulls and the most likely pull number.

gachaprobabilitymonetizationgame-designsimulation

3 tools expose this connector's capabilities to your AI agent.

analyze_monetization_impact

Converts pity simulation data into financial and design-risk metrics

get_pity_summary

Provides a high-level summary of the system feel and statistical central tendencies

calculate_pity_dynamics

Simulates the step-by-step probability mechanics of a gacha system

See how to talk to your AI agent using Gacha Pity Probability Calculator.

Calculate the dynamics for a gacha with a 1% base rate, soft pity starting at pull 50 with a 2% increase per pull, and hard pity at 80 pulls. Simulate 100 pulls.

The simulation for your gacha system shows an expected number of pulls of 54.2 and a whale probability of 0.12. The probability per pull increases steadily from pull 50 until it reaches 100% at pull 80.

Analyze the monetization impact for a system where the expected pulls is 65 and the cost per pull is 2.50 units.

The average cost per obtain is 162.50 units. The estimated revenue per player is also 162.50 units based on the expected pulls.

Give me a summary of the pity dynamics for a system with a 0.5% base rate and hard pity at 90.

The median number of pulls to obtain the item is 72, and the most likely pull is 75. The total success chance at the end of the simulation is 100%.

Use the `calculate_pity_dynamics` tool. Set the `baseRatePercent`, define when the increase starts with `softPityStartPull`, and specify the increment with `softPityRateIncreasePerPull`.

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