# Combat Balance Checker MCP > Combat Balance Checker quantifies turn-based combat outcomes for game designers. It runs large-scale simulations, providing statistical proof on win rates, damage metrics, and stat influence. Need to know if a matchup is stable or overpowered? Use this MCP. ## Overview - **Category:** gaming - **Price:** Free - **Tags:** combat-sim, game-design, statistics, battle-engine, balance-testing ## Description When you're building a turn-based system, you can't just run one fight in the sandbox and call it balanced. You need data that stands up to scrutiny. This MCP is for game designers who need hard numbers on how their combat mechanics perform. It runs thousands of randomized iterations so you get statistically significant metrics—things like true win rates and average turn counts, not just lucky outcomes. By analyzing the fight data, you can pinpoint which attributes, whether it's speed or pure damage, are actually driving the victory margin. You can compare two distinct character builds side-by-side to see exactly what breaks. And if you want a professional stability report, you can run an audit against industry standards. Finding this level of deep analysis used to take days of manual playtesting and spreadsheets. Now, connect your client to the Vinkius catalog; this MCP gives you immediate access to rigorous simulation tools that let you prove whether your combat system actually works. ## Tools ### audit_balance Checks an entire matchup against industry standards to flag potential stability issues or extreme imbalances. ### analyze_influence Determines which specific character attribute, like Speed or Attack, has the greatest impact on a given fight's result. ### simulate_combat Runs 1,000 randomized combat rounds between two defined character profiles to generate raw performance metrics. ## Prompt Examples **Prompt:** ``` Compare a Glass Cannon against a Tank profile. ``` **Response:** ``` Running `simulate_combat` with these profiles will reveal the win rate, average turns, and whether high ATK can overcome high DEF. ``` **Prompt:** ``` Which stat is most important for this matchup? ``` **Response:** ``` By using `analyze_influence`, the engine will identify if SPD or ATK is the primary driver of the outcome. ``` **Prompt:** ``` Is a 70% win rate considered balanced? ``` **Response:** ``` You can check this using `audit_balance`. A 70% win rate will likely be flagged as Skewed or Broken depending on your thresholds. ``` ## Capabilities ### undefined undefined ### undefined undefined ### undefined undefined ### undefined undefined ### undefined undefined ## Use Cases ### The 'Glass Cannon' vs. the 'Tank' A designer needs to know if their high-damage but squishy character (Glass Cannon) can survive a prolonged fight against an armor-heavy opponent (Tank). They use `simulate_combat` with both profiles, which reveals not just the win rate, but also the average number of turns before one profile collapses. ### Finding the Core Stat A team thinks high Attack power is the most important stat. Before committing to it, they use `analyze_influence`. The MCP returns data showing that actually, the opponent's Speed (SPD) determines whether the ATK even lands, forcing them to re-evaluate their core design. ### Checking for Overpowered Matchups A new matchup shows a 90% win rate for one side. The team runs `audit_balance`. Instead of just seeing the number, the MCP flags it as 'Highly Skewed,' forcing them to adjust the rules until the system hits an acceptable stability range. ### Refining a Skill Tree A developer adds a new defensive shield skill but isn't sure if it matters. They run `simulate_combat` comparing the old build to the new one, allowing them to quantify exactly how much the defensive shield changes the average turns required to win. ## Benefits - Get reliable win rates and damage stats immediately. Instead of guessing, you run `simulate_combat` to generate thousands of data points proving the outcome's probability. - Pinpoint weak links in your design. Use `analyze_influence` to stop assuming high ATK is always king; this tool shows if SPD or DEF actually controls the battle flow. - Avoid launching a broken game. Run an official audit with `audit_balance` to see if your current ruleset meets professional stability benchmarks. - Compare builds without coding. You can feed two unique profiles into `simulate_combat` and get a clear performance breakdown, showing exactly where one build loses out. - Stop relying on anecdotal testing. The MCP gives you quantitative data, turning guesswork into measurable design metrics. ## How It Works The bottom line is you get statistical proof of your game's mechanics working under pressure. 1. Define the matchup or character profiles you want to test (e.g., 'Tank' vs. 'Mage'). 2. Invoke the desired function, whether it’s running a large simulation or checking for imbalances. 3. Review the generated metrics, which include win rates and key attribute influence reports. ## Frequently Asked Questions **How does `simulate_combat` work?** It runs 1,000 randomized rounds between two character profiles you supply. This high number of iterations ensures that the resulting metrics are statistically significant and reliable. **What is the difference between `analyze_influence` and `audit_balance`?** `analyze_influence` tells you which single stat (like ATK or DEF) is driving a specific outcome. `audit_balance` checks the entire system against professional stability thresholds. **Can I compare two characters with `simulate_combat`?** Yes, that's exactly what it does. You define two profiles and the MCP simulates their fight to give you a direct comparison of metrics like win rate and average turns. **Is this only for fighting classes?** No. You can use it on any profile setup, allowing you to quantify combat outcomes regardless of the class or attribute focus you're testing. **How many iterations does `simulate_combat` run per request?** It runs 1,000 randomized iterations for every single request. This high volume ensures you receive statistically significant data on win rates and damage metrics. **What data points must I provide when using `analyze_influence`?** You need to supply the raw combat profiles or simulation results. The tool then analyzes those stats to identify which specific attribute, like SPD or ATK, drives the outcome. **How does setting thresholds affect the output of `audit_balance`?** When you run `audit_balance`, you define what professional stability means for your game. The MCP compares the resulting win rate against those specific thresholds to flag imbalances. **Can I use `analyze_influence` after running simulations with `simulate_combat`?** Yes, that's a common workflow. First, run `simulate_combat` to gather initial data; then, pass the resulting dataset to analyze_influence to pinpoint the primary driver of the victory margin. **How accurate are the simulation results?** Every request executes 1,000 independent combat iterations using randomized rolls for critical hits to ensure statistical significance and account for RNG volatility. **What attributes can I compare?** You can compare any profiles containing HP, ATK, DEF, SPD, critRate, and critMult. Use `simulate_combat` to see how these stats interact. **How do I know if a matchup is broken?** Use the `audit_balance` tool. It classifies matchups as Stable, Skewed, or Broken based on whether the win rate stays within acceptable professional bounds.