What is the difference between this MCP and a standard A/B testing capability?

Standard capabilities often rely on p-values. This MCP uses Bayesian inference, which calculates the actual probability that one variant is better than another. It gives you a much deeper understanding of the results.

Do I need to provide prior data for this calculator?

While the MCP is powerful, the calculations rely on the conversion and visitor data you provide in your prompt. The Bayesian approach uses this data to update its probability estimates.

Can I use this for anything other than conversion rates?

The capability is designed for conversion probability and rate comparisons. It focuses on the Beta-Bernoulli relationship, making it ideal for binary outcome testing.

Is this better than a T-test?

Many users find the Bayesian approach more intuitive. Instead of just rejecting or failing to reject a null hypothesis, it gives you a direct probability of superiority, which is often easier to act on.

What does 'expected loss' mean?

Expected loss quantifies the risk. It tells you how much you might lose (in terms of conversions or revenue) if you choose the wrong variant.

What does the superiority probability tell me?

It tells you the likelihood that Variant B's conversion rate is higher than Variant A's, based on your observed data.

How do I use the decision recommendation capability?

Provide your conversion and visitor counts for both variants. You can also set a confidenceThreshold (e.g., 0.95) to define how much certainty you require before the capability recommends choosing Variant B.

What is 'Expected Loss' in this context?

Expected loss quantifies the potential downside risk. It represents the expected reduction in conversion rate if you choose a variant that is actually inferior to the other.

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Connect Bayesian Ab Testing Calculator to ChatGPT and Claude

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Stop guessing. Get the true probability of a winner.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 4 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-04

4 capabilities in this set.

Part of 4 available through Bayesian A/B Calculator.

01
Calculate expected loss
Calculate the expected loss for choosing either variant
02
Calculate expected uplift
Calculate the expected uplift of Variant B over Variant 1
03
Calculate superiority probability
Calculate the probability that Variant B is better than Variant A
04
Evaluate decision recommendation
Evaluate the decision recommendation based on a threshold

Real work

What teams actually run

Optimizing Checkout Flows

You compare two checkout button colors. The MCP calculates the superiority probability, telling you which color is truly better.

Comparing Headline Copy

You test two different value propositions. Use the expected uplift capability to see the potential gain from adopting the winning headline.

Assessing Feature Adoption

A new feature is rolled out. Calculate the expected loss if you prematurely abandon the feature based on limited data.

Prioritizing UI Changes

You have three potential UI changes. Use the decision recommendation capability to see which one meets your 95% confidence threshold.

One connector, every AI

Bayesian A/B Calculator works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put Bayesian A/B Calculator to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

722ms average. Fast in production.

Bayesian A/B Calculator is checked daily against the live service.

Daily averagePeak 900ms

Oct 3Today

Fastest day: 541ms
Slowest day: 900ms
14-day trend: Slowing+20%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 4 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Bayesian A/B Calculator, so you can see the experience inside your AI.

It does not authenticate your account with Bayesian A/B Calculator. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Bayesian A/B Calculator Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_k0dC7IEUCVVW7fYVOUh3T85V4ei1IvThJnPQPt0d/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — Bayesian A/B Calculator capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "bayesian-ab-testing-calculator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_k0dC7IEUCVVW7fYVOUh3T85V4ei1IvThJnPQPt0d/mcp"
    }
  }
}

Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Bayesian A/B Calculator in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Bayesian A/B Calculator owners hand off.

This MCP is built for data-driven teams who need to move beyond basic statistical tests. If you're constantly comparing conversion rates and need to know the true probability of success, this capability gives you the confidence to make big decisions.

01
CRO Specialist
Use it to compare multiple landing page versions and determine which design provides the highest conversion probability.
02
Product Manager
Run analyses on feature adoption rates to quantify the expected uplift of a new product flow.
03
Data Scientist
Leverage the Bayesian inference engine to perform advanced statistical comparisons that standard capabilities miss.

FAQ

Questions Bayesian A/B Calculator owners ask.

01
What is the difference between this MCP and a standard A/B testing capability?
Standard capabilities often rely on p-values. This MCP uses Bayesian inference, which calculates the actual probability that one variant is better than another. It gives you a much deeper understanding of the results.
02
Do I need to provide prior data for this calculator?
While the MCP is powerful, the calculations rely on the conversion and visitor data you provide in your prompt. The Bayesian approach uses this data to update its probability estimates.
03
Can I use this for anything other than conversion rates?
The capability is designed for conversion probability and rate comparisons. It focuses on the Beta-Bernoulli relationship, making it ideal for binary outcome testing.
04
Is this better than a T-test?
Many users find the Bayesian approach more intuitive. Instead of just rejecting or failing to reject a null hypothesis, it gives you a direct probability of superiority, which is often easier to act on.
05
What does 'expected loss' mean?
Expected loss quantifies the risk. It tells you how much you might lose (in terms of conversions or revenue) if you choose the wrong variant.
06
What does the superiority probability tell me?
It tells you the likelihood that Variant B's conversion rate is higher than Variant A's, based on your observed data.
07
How do I use the decision recommendation capability?
Provide your conversion and visitor counts for both variants. You can also set a confidenceThreshold (e.g., 0.95) to define how much certainty you require before the capability recommends choosing Variant B.
08
What is 'Expected Loss' in this context?
Expected loss quantifies the potential downside risk. It represents the expected reduction in conversion rate if you choose a variant that is actually inferior to the other.

Explore

More in Analytics

Connect Bayesian A/B Calculator to Claude, Cursor, ChatGPT & more

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-04

Calculate expected loss

Calculate expected uplift

Calculate superiority probability

Evaluate decision recommendation

What teams actually run

Optimizing Checkout Flows

Comparing Headline Copy

Assessing Feature Adoption

Prioritizing UI Changes

Building your own app? The connector is yours to use.

Bayesian A/B Calculator Connector

Claude Desktop

CRO Specialist

Product Manager

Data Scientist

What is the difference between this MCP and a standard A/B testing capability?

Do I need to provide prior data for this calculator?

Can I use this for anything other than conversion rates?

Is this better than a T-test?

What does 'expected loss' mean?

What does the superiority probability tell me?

How do I use the decision recommendation capability?

What is 'Expected Loss' in this context?

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