Counterfactual-Variant MCP for AI. Prove logic works on variables that change.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Connect to your AI in seconds.
Counterfactual-Variant Prover forces your AI agent to prove logic from first principles, preventing it from reciting standard answers on modified problems.
It intercepts pattern-matching failures, isolating variables and mapping rule discrepancies so you can verify complex puzzle solutions are logically sound.
What your AI can do
Validate counterfactual
Structured reflection tool to force the agent to isolate variables, map rule differences against classic versions, and trace calculations from first principles using only modified values.
It detects if a modified logic problem resembles a well-known template and identifies the deviation.
The tool extracts every number, rule, or constant from your prompt, ensuring no memory leakage contaminates the calculation.
It explicitly compares the puzzle's given rules against the classic version to highlight what has changed and what must be ignored.
The agent calculates the solution step-by-step, using only the isolated variables and modified rules you provided.
It verifies that the resulting answer is not simply a memorized template response but genuinely derived from the unique problem constraints.
Ask an AI about this
Waiting for input…
Counterfactual-Variant Prover: 1 Tool
This single tool helps your agent isolate variables, map rule discrepancies, and trace calculations step-by-step for complex puzzle solutions.
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 Counterfactual-Variant Prover on VinkiusValidate Counterfactual
Structured reflection tool to force the agent to isolate variables, map rule differences against classic versions, and trace calculations...
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
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 Counterfactual-Variant 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
- Create Agent Skills with progressive disclosure
- 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 Counterfactual-Variant Prover, 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
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Counterfactual-Variant Prover. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
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 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The Problem of AI Memory
Right now, when you ask a general-purpose agent to solve a logic puzzle or math problem, it sometimes fails in subtle ways. It looks convincing, but that confidence is misleading because the AI often falls back on its training data—it just recites the classic answer for Monty Hall or the river crossing, even if you changed three variables.
It's like asking someone to calculate a route using new road closures, and they give you directions based on how the roads *used* to be. This MCP stops that. It forces your agent to map the differences between the standard problem and yours, then calculates everything from scratch.
Counterfactual-Variant Prover: Proven Logic
You no longer have to manually check if a slight change in your input variables causes the AI to default back to a known, textbook answer. The tool handles this rigorous validation by forcing steps like isolating all constants and mapping every rule discrepancy.
What you get now is genuine proof of logic. Your agent can't just tell you an answer; it has to show you how it got there using only the rules you set.
What your AI can actually do with this
AI models are trained on millions of classic puzzles—things like the Monty Hall problem or river crossings. When you feed them a slightly changed version of that puzzle, they often don't solve it; they just recite the standard answer. This MCP stops that pattern-matching failure. It forces your AI agent to do something much harder: prove its logic step by step, using only the variables and rules you provide.
You can trace every single calculation back to a starting point, ensuring nothing was pulled from memory. Connecting this through Vinkius gives any compatible client access to these rigorous checks, letting you validate complex reasoning across everything from academic research to QA testing.
019e5a44-fb52-71a4-8c5b-bb49538eb624 
Here's how it actually works
The bottom line is you get an audit trail proving that the AI's final answer was earned through fresh logic, not pulled from its training data.
Input a complex logic puzzle or math problem into your agent, asking it to use this MCP for validation.
The system runs its internal checks: it identifies if the prompt resembles a classic template and then forces the AI client to isolate all variables and map how they differ from the standard rules.
Finally, the tool executes a calculation using only those isolated inputs. You get back a proof that verifies whether the solution is logically sound or merely a contaminated recitation.
Who is this actually for?
Academic researchers and QA engineers who test complex reasoning systems. If your work relies on LLMs solving puzzles or doing math based on niche rules, you need this. It stops 'hallucinated' intelligence.
Testing new models to ensure they solve modified benchmark problems without falling back onto memorized solutions from the training set.
Validating AI performance on niche or highly customized logic puzzles where standard textbook answers don't apply.
Building test suites that specifically challenge LLMs to prevent them from giving canned, incorrect responses when input parameters change slightly.
What Changes When You Connect
It stops data recitation. If your agent is supposed to solve a puzzle but just spits out the Wikipedia answer, this tool catches it immediately.
You gain verifiable proof of calculation steps. Instead of just getting an 'answer,' you get an audit showing which variables were used and how they changed the outcome.
Use validate_counterfactual when dealing with complex decision trees or rule-based logic puzzles that have been slightly customized for your use case.
It prevents variable contamination. The tool forces separation between constants from the original puzzle and the new inputs, keeping your calculations clean.
This is critical for building reliable AI agents. It moves reasoning past simple pattern matching into true first-principles application.
See it in action
Testing modified riddle sets
An academic researcher wants to test if a new LLM can solve variations of the Prisoner's Dilemma. Without this MCP, the AI might assume standard constraints. Using validate_counterfactual forces it to map the specific rules of the new scenario and prove its logic only relies on those inputs.
QA testing for variable leakage
A QA engineer needs to confirm that changing a single parameter (like boat capacity in a river crossing puzzle) doesn't cause the AI to default back to the standard, original solution. The tool confirms that the calculation shifts entirely based on the modified input.
Evaluating complex ethical scenarios
A product team uses this when testing variations of classic trolley problems. They need proof that changing the number of people or the source of the threat actually changes the calculated optimal action, rather than just reciting the standard moral dilemma response.
The honest tradeoffs
Assuming basic reasoning is enough
Prompting an agent with a modified logic puzzle and accepting its confident answer without verification. The AI might be using boilerplate knowledge.
Always run the prompt through validate_counterfactual. This ensures that any final conclusion was derived from your specific constraints, not general training data.
Skipping variable isolation
Trying to solve a modified puzzle by simply asking 'What is the answer?' The agent will use its memory and contaminate the result.
validate_counterfactual handles this automatically. It forces the system to extract, map, and use only the variables explicitly provided in your prompt.
When It Fits, When It Doesn't
Use this MCP if your problem's correctness depends on maintaining strict logical integrity when dealing with variations of classic puzzles or mathematical benchmarks. If you are testing a new rule set—like changing weights in a physics simulation or altering the number of parties in a game theory scenario—this tool is essential. Don't use it if you just need general text summarization, creative writing, or simple data retrieval; those tasks don't require counterfactual proofing. If your goal is simply 'get an answer,' but that answer must be provable against modified inputs, this is the right choice.
Questions you might have
How does Counterfactual-Variant Prover stop recitation bias? +
By introducing structural friction. When an agent is forced to fill a schema requiring explicit separation of variables, mapping of differences, and step-by-step logic, it cannot rely on automatic token generation. The tool rejects any attempt to skip these steps or leak classic parameters.
What happens if a puzzle has no classic equivalent? +
If no classic signature is detected, the model sets recitationSignatureDetected to false, maps variables, and solves it. However, if the text contains keywords of known puzzles (e.g. Monty Hall, Cheryl), the engine enforces the full counterfactual check to avoid semantic traps.
Can it be used alongside other reasoning provers? +
Yes. It works as an orthogonal check. While the Critical Thinking Prover checks overall cognitive quality, the Counterfactual-Variant Prover focuses specifically on variable isolation and preventing memorization loops in logic and mathematics.
How does Counterfactual-Variant Prover handle complex or proprietary variables? +
It processes all input text within your secure client environment. You provide the puzzle details and variables in plain text, and the MCP handles the data according to standard privacy protocols.
What happens if a calculation step fails during the validate_counterfactual process? +
The tool pinpoints exactly which of its five decision pivots failed. It will report whether variable isolation was incomplete, or where the first-principles calculation broke down due to an unmapped rule discrepancy.
Which AI clients are compatible with Counterfactual-Variant Prover via MCP? +
It connects through any client supporting the Model Context Protocol standard. This includes major tools like Cursor, Claude Desktop, Windsurf, and VS Code integrations.
What should I do if I hit a rate limit when using Counterfactual-Variant Prover? +
Vinkius manages the resource usage. If you encounter a rate limit error, wait for the specified cooldown period or check your subscription plan details to increase your allowed call volume.
Can I pass structured data (like lists of numbers) into Counterfactual-Variant Prover? +
Yes. Structure is key; ensure all numeric constants and parameters are clearly defined in the prompt. The tool relies on your agent to extract these variables before executing validate_counterfactual.
Powerful workflows you can unlock today
We've already built the connector for Counterfactual-Variant. Just plug in your AI agents and start using Vinkius.
No hosting. No infrastructure. No complex setup.
All 1 tools are live and waiting.
You're up and running in seconds.
Gemini Vinkius gives your AI agents access to the full catalog of app connectors, all fully managed, secure, and enterprise-ready. One subscription, every tool you need.
Built, hosted, and secured by Vinkius. You just connect and go.