MCP Servers to Debug Engineering Decisions.
Base rates corrected, framing traps exposed, logic proven step-by-step , debug every decision your AI makes before you act on it
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent receives a decision that depends on data: 'Our A/B test shows the new checkout flow increased conversion by 12%.
We should roll it out to all users.' Phase 1: the agent runs `validate_probabilistic_clarity`. Intuition vs. Computation: the 12% lift sounds great.
But what is the p-value? With 500 users per variant, a 12% lift on a 3% baseline conversion rate means the difference is 3.0% vs.
3.36%. At n=500, that difference is not statistically significant (p=0.34). The intuitive '12% improvement' hides the fact that this could easily be random noise.
Base Rate Correction: the baseline conversion rate is 3%. A 12% relative lift is only 0.36 percentage points absolute. Prior probability that any random UI change produces a 0.36pp lift: approximately 40% (most A/B tests show no effect).
The posterior probability that this is a real effect, using Bayesian reasoning, drops to around 55%. Not the 95% confidence you assumed.
Sample Audit: 500 users per variant. Selection bias check , were these first-time visitors or returning customers? If returning customers, they already know your checkout flow and the test measures familiarity preference, not design quality.
Survivorship bias , did you count users who started checkout or only those who completed it? Framing Deconstruction: '12% increase in conversion' frames this as a success.
Reframe: '88% of the improvement might be noise.' The question framing changes the decision. Independence: are conversion and the new design truly independent of seasonality? If you ran the test during a promotional period, the variant correlation might be the promotion, not the design.
Phase 2: the agent runs `validate_counterfactual`. Variables isolated: sample size (500), baseline rate (3%), observed lift (0.36pp), test duration (7 days), user segment (mixed).
Rule discrepancies: the standard A/B test interpretation assumes independent samples and normal distribution. But conversion is binary (yes/no), making binomial distribution more accurate.
The agent recalculates using the exact binomial test instead of the normal approximation. First-principles derivation: using binomial distribution with n=500, p_control=0.030, p_variant=0.0336, the exact p-value is 0.38, confirming the result is not statistically significant.
The agent traces every step: define the null hypothesis, compute the test statistic, apply the correct distribution, derive the p-value.
No recitation of 'typical A/B test significance' , pure mathematical proof. Final output: the 12% conversion lift is not statistically significant.
Do not roll out. Extend the test to 5,000 users per variant to achieve 80% statistical power for detecting a 12% relative lift.
MCP Server Orchestration: 2 MCP Servers, one intelligent agent
Connect Marilyn vos Savant Probabilistic Clarity Prover and Counterfactual-Variant Prover MCP servers so your AI agent checks every data-driven conclusion against five statistical failure modes and then forces step-by-step logical proof to eliminate pattern-matching bias. Engineering and product teams making data-driven decisions get a two-phase cognitive audit: first, the agent checks whether your conclusion neglects base rates, trusts a biased sample, accepts a rigged question framing, confuses correlation with causation, or relies on gut intuition over computed probability. Then, it takes your logical reasoning and forces the agent to isolate every variable, find rule discrepancies in your assumptions, and derive the answer from first principles instead of reciting the standard solution. The result is decision-making that survives both statistical and logical scrutiny.
Marilyn Vos Savant Probabilistic Clarity Prover
triggerAudits conclusions against five statistical checks: base rates, sample bias, framing, independence, and probability computation
validate_probabilistic_clarity Counterfactual Variant Prover
actionForces step-by-step logical derivation from first principles, preventing pattern-matching recitation bias
validate_counterfactual Run This Automation Today
Connect Claude, ChatGPT, Cursor, or any AI agent to the Vinkius catalog and run this automation in minutes.
Build Your Own MCP
Turn any internal API into an MCP server. 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
Connect & Automate
The 2 servers this recipe uses are ready in the catalog. Connect them once, paste a prompt, and your AI runs the full workflow.
- Marilyn Vos Savant Probabilistic Clarity Prover & Counterfactual Variant Prover ready in the catalog right now
- Add more from 4,700+ servers whenever you need
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added every week
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 4,700+ MCP servers and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across every tool, in one conversation. That's what this infrastructure was built for.
Cross-Platform Intelligence
Your agent doesn't just connect to tools. It understands the relationships between them. Data flows where it needs to go, automatically, with full context preserved across every platform.
Contextual Reasoning
Every decision your agent makes considers the full picture. It reads CRM data, checks calendars, reviews conversation history, and acts on everything at once. Not step by step. All at once.
Productivity at Scale
What used to take 45 minutes across five different dashboards now takes one sentence. Your agent runs the entire workflow end to end while you focus on decisions that actually matter.
Zero-Config Reliability
No API keys to paste. No webhooks to configure. No YAML to debug. Connect your MCP servers once, and your agent handles the rest. Every time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius MCP catalog to handle these for you. You describe what you need. It does the rest.
Data analysts interpreting A/B test results who need statistical validation before recommending rollout decisions to product teams
Engineering managers evaluating performance benchmarks who need to verify that observed improvements are statistically significant and not measurement artifacts
Product teams making pricing decisions based on survey data who need to check whether the sample is representative and the framing is neutral
Startup founders interpreting early traction metrics who need to distinguish signal from noise in small-sample datasets
Frequently Asked Questions About This MCP Server Orchestration
Which MCP servers do I need for this workflow?
Two: Marilyn vos Savant Probabilistic Clarity Prover and Counterfactual-Variant Prover. Connect both to your AI client.
Does this work with Claude Desktop, Cursor or Windsurf?
Yes. Any AI client that supports the Model Context Protocol works , Claude Desktop, Cursor, Windsurf, Cline and others.
Do I need to be a statistician to use this?
No. The workflow translates statistical concepts into plain language. It tells you the 12% lift is noise, explains why, and gives you the exact sample size needed to get a real answer. You act on the recommendation, not the math.
Why does the Counterfactual Prover recalculate what the Probabilistic Prover already checked?
The Probabilistic Prover checks the statistical assumptions , base rates, sample bias, framing. The Counterfactual Prover recalculates the math from first principles using the correct distribution. Sometimes the conclusion is the same, but the Counterfactual Prover catches cases where the standard statistical test itself was the wrong choice.
Can this workflow validate machine learning model performance claims?
Yes. Feed it any claim like '95% accuracy on medical diagnosis.' The Probabilistic Prover will check which benchmark, what baseline, and sample representativeness. The Counterfactual Prover will verify whether the accuracy metric is the right measure for the problem or if precision and recall matter more.
How does it handle small sample sizes?
It does not reject small samples , it quantifies their limitations. At n=50, it calculates the confidence interval width and tells you exactly how many more observations you need to reach a statistically meaningful conclusion.
MCP servers used in this workflow
Marilyn vos Savant Probabilistic Clarity Prover
Marilyn vos Savant Probabilistic Clarity Prover forces your AI client to check every conclusion against five critical statistical checks: base rates, sample bias, event independence, framing, and raw probability computation. It stops the AI from trusting its gut answer—it proves the math first.
Counterfactual-Variant Prover
Counterfactual-Variant Prover is an MCP Server that forces agents to prove complex logical derivations. It stops AI models from reciting standard answers by requiring structured steps: isolating variables, mapping rule discrepancies, and calculating from first principles. Use this tool when you need to verify a solution under modified or contradictory rules, preventing pattern-matching errors in logic puzzles.