Bayesian A/B Calculator Connector for AI agents.
4 live capabilities
Get clear win probabilities and risk assessments for your conversion tests.
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Why people use Bayesian A/B Calculator
Bayesian A/B Testing Calculator for Conversion Rate Optimization
With this Connector, you just tell your AI client the numbers. It handles the Bayesian math instantly and tells you the probability of winning and the risk of losing. You get a clear ship it answer in seconds.
What Vinkius changes
You get a data-backed go or no-go instead of a confusing p-value.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Landing Page Optimization
A marketer has two designs and wants to know the probability of one winning before the traffic dies out.
- Real-world use case 02
Feature Flag Validation
A product manager needs to see the expected loss of rolling out a new button to 100% of users.
- Real-world use case 03
Email Campaign Analysis
An analyst wants to see the expected uplift of a new subject line to justify a full rollout.
Complete set · 4capabilities
The complete Bayesian A/B Calculator capability set.
These are the exact actions your AI can choose when you ask it to work with Bayesian A/B Calculator.
01—04
4 capabilities in this set.
Part of 4 available through Bayesian A/B Calculator.
- 01 Capability
Calculate expected loss
This calculates the potential downside you face by choosing one variant over the other. It lets you see the actual risk in terms of conversion units rather than abstract math.
- 02 Capability
Calculate expected uplift
This gives you a concrete number on how much better Variant B is expected to perform compared to Variant A. It's the best way to project the real-world impact of a successful test.
- 03 Capability
Evaluate decision recommendation
This capability takes your data and a specific confidence threshold to give you a clear recommendation. It tells you exactly whether to ship the winner or keep testing.
- 04 Capability
Calculate superiority probability
This capability tells you the actual percentage chance that Variant B is outperforming Variant A. It helps you move past binary win/loss thinking to see the true confidence of your data.
Set up in minutes
One URL. Then ask Bayesian A/B Calculator to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Bayesian A/B Calculator from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_k0dC7IEUCVVW7fYVOUh3T85V4ei1IvThJnPQPt0d/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Bayesian A/B Calculator, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Bayesian A/B Calculator for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_k0dC7IEUCVVW7fYVOUh3T85V4ei1IvThJnPQPt0d/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Bayesian A/B Calculator URL.
- Step 03
Save and start
Save the connection and enable Bayesian A/B Calculator in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"bayesian-ab-testing-calculator": {
"url": "https://edge.vinkius.com/vk_preview_k0dC7IEUCVVW7fYVOUh3T85V4ei1IvThJnPQPt0d/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Bayesian A/B Calculator
Open Agent mode in chat and ask: "Using Bayesian A/B Calculator, help me...". 4 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"bayesian-ab-testing-calculator": {
"url": "https://edge.vinkius.com/vk_preview_k0dC7IEUCVVW7fYVOUh3T85V4ei1IvThJnPQPt0d/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Bayesian A/B Calculator
Ask Copilot: "Using Bayesian A/B Calculator, help me...". 4 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"bayesian-ab-testing-calculator": {
"url": "https://edge.vinkius.com/vk_preview_k0dC7IEUCVVW7fYVOUh3T85V4ei1IvThJnPQPt0d/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Bayesian A/B Calculator
Open Cascade and ask: "Using Bayesian A/B Calculator, help me...". 4 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"bayesian-ab-testing-calculator": {
"url": "https://edge.vinkius.com/vk_preview_k0dC7IEUCVVW7fYVOUh3T85V4ei1IvThJnPQPt0d/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Bayesian A/B Calculator
Ask Cline: "Using Bayesian A/B Calculator, help me...". 4 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add bayesian-ab-testing-calculator --transport http "https://edge.vinkius.com/vk_preview_k0dC7IEUCVVW7fYVOUh3T85V4ei1IvThJnPQPt0d/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Bayesian A/B Calculator
Ask Claude: "Using Bayesian A/B Calculator, show me...". 4 tools are ready
Where the request belongs
Work Bayesian A/B Calculator can move forward.
Growth marketers who need to justify product changes to stakeholders and data scientists who want to provide more actionable insights than just a significance score.
Growth Marketer
Deciding which landing page version to keep without waiting weeks for significance.
Product Manager
Validating new feature impacts with a clear understanding of the risk of a rollback.
Data Analyst
Moving from reporting what happened to recommending what to do next using Bayesian math.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse Connectors
AB Test Sample Size Calculator
Calculate required sample size, test duration, and peeking risk for A/B experiments.
A/B Test Significance Calculator
Calculate statistical significance, required sample sizes, and power for A/B tests.
Test Duration Calculator
Calculate required A/B test duration, sample sizes, and experiment risk levels.
Multivariate Test Analyzer
Perform 2k factorial analysis to identify optimal element combinations and interaction effects in multivariate experiments.
Incrementality Estimator
Determines true campaign ROI by calculating lift above natural conversion rates using control group data.
Data Analysis Prover
A marketing team asked an AI to analyze campaign data. The AI reported 'significant correlation between email frequency and purchase rate (p<0.05).' The team tripled emails. Unsubscribes spiked 340%. Sample: N=47 self-selected respondents, no power analysis. Correlation: observational, no confounders. Distribution: right-skewed but mean used. p=0.043 but Cohen's d=0.12. trivial. Chart: truncated Y-axis making a 2% difference look enormous. This capability forces five axes: sample validity, causal inference, distribution awareness, significance with effect size, and visualization integrity.
Bring your own AI
Change the model, client or framework. Keep Bayesian A/B Calculator connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Bayesian A/B Calculator.
The practical details behind the request, access and result.
What is the Bayesian A/B Testing Calculator?
It is a capability that helps you understand the real probability of one variant winning over another in an A/B test, rather than just giving you a p-value.
How does this help with conversion rates?
It takes your conversion data and calculates the actual likelihood of success, helping you decide which version to ship with more confidence.
Can I use it for low-traffic tests?
Yes, Bayesian inference is often better for low-traffic scenarios because it provides a probability of winning rather than waiting for a high sample size.
What is expected loss in A/B testing?
Expected loss quantifies the risk of choosing a variant that might actually perform worse than the current winner, helping you balance risk and reward.
Why use Bayesian instead of p-values?
Bayesian methods are often more intuitive for business decisions because they tell you the probability of a result being true, which is easier to explain to stakeholders.
How do I get a go or no-go recommendation?
By providing your test data and a confidence threshold, the capability evaluates the results and tells you clearly whether the data supports a rollout.
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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