MCP For Claude Marketplace
D&D Character Stat Roller

D&D Character Stat Roller MCP Connector for Claude

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Statistical simulation engine for evaluating D&D attribute generation methods.

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

This MCP server provides a high-performance statistical simulation engine to compare different D&D character attribute generation methods. By executing 10,000 randomized iterations per method, it allows AI agents to analyze the mathematical 'power' and consistency of mechanics like heroic_4d6, classic_33d6, point_buy, and standard_array. Users can use tools like simulate_method to generate datasets, analyze_attribute_averages to find mean scores, and evaluate_power_threshold to determine the probability of high-strength character creation. It is an essential tool for tabletop RPG designers and players looking to understand the impact of different rolling mechanics on character strength.

dndttrpgdice-rollingprobabilitycharacter-creation

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

analyze_attribute_averages

Calculates the mean value for each attribute across a provided set of simulation results

evaluate_power_threshold

Determines the probability of a character "outperforming" a specific power metric

generate_method_comparison_report

Produces a high-level comparative summary between two different simulation datasets

simulate_method

Executes a large-scale randomized simulation for a specific generation method

See how to talk to your AI agent using D&D Character Stat Roller.

Run a simulation for the heroic 4d6 method and show me the average strength.

The simulation for `heroic_4d6` is complete. The calculated average strength across 10,000 iterations is 12.38.

What is the probability of getting at least two attributes of 15 or higher using the classic 3d6 method?

Using the `classic_3d6` dataset, the probability of having at least 2 attributes with a score of 15 or higher is 4.2%.

Compare the power of point buy versus standard array.

The comparison report shows that `point_buy` results in a higher average for primary attributes, while `standard_array` provides more consistent baseline scores across all six attributes.

You can simulate `heroic_4d6`, `classic_3d6`, `point_buy`, and `standard_array` methods.

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