MCP Servers for Reliable A/B Test Analysis.
A/B test results interrogated for hidden assumptions, statistical validity verified before shipping , stop making product decisions on p-values alone
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your growth team runs an A/B test on the pricing page: variant B (annual pricing displayed first) shows +12% conversion rate with p=0.03.
The team wants to ship immediately. Phase 1: the agent runs `validate_critical_thinking`. Hidden Assumptions: (1) conversion rate is the right metric , but what about revenue per user? If annual plans have lower LTV due to higher refund rates, +12% conversions could mean -5% revenue.
(2) The test ran for 2 weeks , does it capture a full purchase cycle? Enterprise buyers take 30-60 days.
(3) 'Displayed first' assumes ordering effect, but the variant also changed copy, button color, and plan names simultaneously. Which change drove the result? Competing Frameworks: Statistical , p=0.03 passes the 0.05 threshold.
But the economic framework asks: is +12% conversion worth the implementation cost and risk of breaking the checkout flow? The UX framework asks: does displaying annual pricing first create confusion for monthly-oriented users? Steelmanned Case Against Shipping: 'The +12% may represent selection bias , annual-first framing attracts price-conscious buyers who compare total costs.
These buyers may have higher support costs and higher churn at renewal. The 2-week window is too short to measure renewal behavior.
Shipping this change optimizes a leading indicator (conversion) at the potential expense of a lagging indicator (LTV).' Second-Order Consequences: if annual-first framing attracts more annual plans, monthly revenue becomes lumpier and harder to forecast.
Finance will need to adjust revenue recognition. Customer success will need to adjust renewal playbooks. The pricing page change cascades into operations.
Phase 2: the agent runs `validate_data_analysis`. Sample Size: 4,200 visitors per variant. For a +12% lift on a 3.5% base conversion, minimum detectable effect at 80% power requires 3,800 per variant.
Sample is sufficient , barely. P-value: 0.03. But the team tested 4 metrics (conversion rate, revenue per user, time on page, bounce rate).
With 4 comparisons, Bonferroni correction sets the threshold at 0.05/4 = 0.0125. At p=0.03, the result does NOT pass multiple comparison correction.
Regression to Mean: the test started during a promotional period (30% off annual plans). The +12% may reflect promotion responsiveness, not layout effect.
A holdout test during non-promotional period is needed. Segment Heterogeneity: breaking results by segment reveals that +12% overall masks +28% for SMB and -3% for Enterprise.
The variant helps small buyers and hurts large buyers , which segment matters more for revenue?
MCP Server Orchestration: 2 MCP Servers, one intelligent agent
Connect Critical Thinking Prover and Data Analysis Prover MCP servers so your AI agent interrogates A/B test results before they become product decisions. Phase 1: the agent runs the Critical Thinking Prover to expose hidden assumptions in the test design, apply competing frameworks (statistical vs. economic vs. user experience), steelman the case for NOT shipping the winning variant, and map second-order consequences of the change. Phase 2: the agent runs the Data Analysis Prover to verify statistical validity , sample size sufficiency, multiple comparison corrections, regression to the mean risks, and segment-level heterogeneity. The result is A/B test interpretation that goes beyond 'variant B wins, ship it' to 'variant B wins for segment X under conditions Y, with these caveats and these risks.'
Critical Thinking Prover
triggerExposes test design assumptions, applies competing frameworks, steelmans non-shipping case, maps second-order effects
validate_critical_thinking Data Analysis Prover
actionVerifies statistical validity , sample size, multiple comparisons, regression to mean, segment heterogeneity
validate_data_analysis 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.
- Critical Thinking Prover & Data Analysis 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.
Growth marketing teams interpreting A/B test results who need systematic verification that statistical significance is real after accounting for multiple comparisons and segment heterogeneity
Product managers making ship/no-ship decisions based on experiments who need a framework that goes beyond p-values to include economic impact, UX consequences, and operational cascades
Data analysts presenting experiment results to stakeholders who need to anticipate and address the critical questions that a sophisticated audience will ask about test validity
Marketing directors justifying or challenging test-driven changes who need rigorous analytical backing to make or defend decisions against pressure to 'just ship the winner'
Frequently Asked Questions About This MCP Server Orchestration
Which MCP servers do I need?
Two: Critical Thinking Prover and Data Analysis Prover.
Does this work with Claude Desktop, Cursor or Windsurf?
Yes. Any AI client that supports the Model Context Protocol works.
Does this replace our experimentation platform?
No. Your platform provides the data. This workflow provides the analytical rigor to interpret that data correctly , catching multiple comparison errors, segment heterogeneity, and second-order consequences that dashboards do not surface.
What if our team does not have a data scientist?
That is exactly when this workflow is most valuable. It provides the statistical rigor a data scientist would apply , sample size validation, multiple comparison correction, selection bias detection , without requiring the team to have that expertise in-house.
Can this work for multivariate tests?
Yes, and it becomes even more critical. Multivariate tests multiply the comparison problem , testing 3 variables with 3 levels each creates 27 combinations. The multiple comparison correction prevents declaring winners from noise.
Stress-Test Hot Takes Before Publishing via MCP
Hidden assumptions exposed, counterarguments steelmanned, source bias detected , publish contrarian takes that survive intellectual combat
How to Fact-Check Data Content Using MCP
Every claim source-verified, every statistic methodology-audited, every bias exposed , publish data-driven content that withstands scrutiny
MCP Recipe for Board-Ready Marketing Reports
Monthly marketing reports transformed from dashboard screenshots to strategic intelligence , vanity metrics eliminated, causal insights surfaced, executive action driven
MCP Recipe to Find Top Revenue Channels
Attribution models stress-tested with first principles, statistical methodology audited for false confidence , make budget decisions on truth, not dashboards
MCP servers used in this workflow
Critical Thinking Prover
Critical Thinking Prover. This tool forces an AI agent to validate its own reasoning before stating a conclusion. It makes the agent surface hidden assumptions, apply multiple competing frameworks, weigh evidence for and against, and map second-order consequences. It prevents the agent from giving overly confident, single-perspective answers.
Data Analysis Prover
The Data Analysis Prover runs any statistical claim through five mandatory checks—sample quality, causal validity, distribution assumptions, effect size reporting, and chart honesty. It forces your AI client to act like a senior statistician reviewing a research paper, catching flaws that standard models miss.