Combat Balance Checker MCP for AI. Prove your combat mechanics work under pressure.
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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.
What your AI can do
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.
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Combat Balance Checker: 3 Tools
These tools let you simulate battles, check for system imbalances, or analyze which character stats are most impactful on the final outcome.
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 Combat Balance Checker on VinkiusAudit 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...
Simulate Combat
Runs 1,000 randomized combat rounds between two defined character profiles to...
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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
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The Combat Balance Checker integration is available immediately — no restart needed.
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
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Combat Balance Checker, 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
- Add new capabilities to your AI anytime you want
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
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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.
Combat testing used to mean hours of manual spreadsheet work.
You know the drill: you build a new class or tweak damage numbers, so you run it through your test environment. You manually record fight outcomes, copy data into spreadsheets, and spend hours calculating averages for win rates and turn counts. It's tedious, error-prone work that only gives you a sense of what *might* be wrong.
With this MCP, the process is instantaneous. You feed it your character profiles, and the system handles thousands of randomized simulations automatically. The result isn't just data; it’s hard evidence showing exactly how often one side wins and which stats mattered most in those outcomes.
The Combat Balance Checker MCP delivers actionable analysis.
You no longer have to guess if a matchup is stable. Running `audit_balance` provides an instant, professional assessment against established industry standards. Furthermore, you can use `analyze_influence` to stop debating which stat matters—the MCP tells you definitively.
The difference is that you move from anecdotal evidence to quantitative proof. You know your combat system works because the data proves it.
What your AI can actually do with this
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.
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Here's how it actually works
The bottom line is you get statistical proof of your game's mechanics working under pressure.
Define the matchup or character profiles you want to test (e.g., 'Tank' vs. 'Mage').
Invoke the desired function, whether it’s running a large simulation or checking for imbalances.
Review the generated metrics, which include win rates and key attribute influence reports.
Who is this actually for?
Game designers who are tired of manual, anecdotal playtesting. System analysts needing hard data to justify combat rules. Playtesters who need a consistent way to stress-test overpowered matchups.
Uses this MCP to determine if a new class or weapon breaks the game's core balance before development begins.
Runs statistical audits on existing combat data to identify underlying attribute dependencies and structural weaknesses.
What Changes When You Connect
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.
See it in action
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.
The honest tradeoffs
Running a single test fight
A designer runs their two favorite builds once in the sandbox and declares it 'balanced' because they won. This is meaningless; one lucky outcome doesn't prove anything.
You must run simulate_combat to generate 1,000 randomized rounds. The resulting statistical average gives you reliable data instead of a single anecdote.
Assuming the stat importance
The team decides that because damage is high, Attack must be the key factor in every fight, wasting time adjusting stats based on gut feeling.
Use analyze_influence to let the data guide you. This tool isolates which attribute truly drives the victory margin, regardless of what your team thinks it should be.
Only checking win rates
A matchup has a 50/50 win rate, so the designer assumes perfect balance and moves on. But they miss that one side always wins in fewer turns.
Check the full picture with audit_balance. This tool reviews metrics beyond just the win percentage to ensure systemic stability across all thresholds.
When It Fits, When It Doesn't
Use this MCP if your primary need is statistical proof of system balance. If you're designing a complex, turn-based combat system and you can’t back up your rules with quantifiable data—the win rates, the stat dependencies—you shouldn't commit to them. The toolset here is perfect for stress-testing: use simulate_combat to get raw data; follow up with analyze_influence to understand why that data looks a certain way; and finish by running audit_balance to ensure the overall system hasn't tipped into an unbalanced state. Don't use this if you just need simple art assets or lore ideas; those tools are for creative writing, not math. This is strictly for mechanical verification.
Questions you might have
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.
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