What kind of experiments can I run with this MCP?

This MCP is designed for 2k Factorial Design of Experiments (DOE) and multivariate testing.

What data do I need to provide for calculations?

To calculate main effects, you must provide the levels, conversions, and visits for each combination in your experiment.

Can I detect if two elements work better together?

Yes, you can use the analyze_interaction_effects capability to detect dependencies between pairs of factors.

Which AI clients can I use with this MCP?

You can use this MCP with any compatible client like Claude, Cursor, Windsurf, or VS Code.

How does the MCP find the best test result?

The identify_winning_combination capability analyzes the data to pinpoint the statistically optimal configuration.

What is the purpose of calculate_main_effects?

This capability answers which individual elements or variants are driving the most significant change in conversions by calculating the average performance impact of switching a factor from its baseline to its variant level.

How can I detect if two elements work better together?

Use the analyze_interaction_effects capability. It compares the observed performance of two-factor combinations against the expected performance if they acted independently, identifying significant dependencies.

How do I find the best configuration for my test?

The identify_winning_combination capability identifies the specific setup with the highest conversion rate and verifies if it is statistically significantly better than the baseline using error variance analysis.

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Connect Multivariate Test Analyzer 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. Find the exact element combinations that drive conversions.

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 · 3 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-03

3 capabilities in this set.

Part of 3 available through Multivariate Test Analyzer Engine.

01
Analyze interaction effects
This capability analyzes how pairs of factors interact with one another. It helps you see if the effect of one element changes based on the presence of another.
02
Calculate main effects
This capability calculates the main effects for each factor in your experiment. You need to provide the levels, conversions, and visits for each combination.
03
Identify winning combination
This capability finds the statistically optimal configuration from your experiment data. It points your agent directly to the winning set of factors.

Real work

What teams actually run

E-commerce UX Testing

Test how different combinations of product images, button colors, and discount placements affect checkout rates.

Landing Page Optimization

Analyze how headline variants and CTA text interact to drive user conversions.

Marketing Campaign Analysis

Evaluate how different audience segments respond to various combinations of ad copy and imagery.

Feature Rollout Evaluation

Determine the impact of new UI elements when they are introduced alongside existing site features.

One connector, every AI

Multivariate Test Analyzer Engine 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 Multivariate Test Analyzer Engine 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

704ms average. Fast in production.

Multivariate Test Analyzer Engine is checked daily against the live service.

Daily averagePeak 920ms

Oct 3Today

Fastest day: 567ms
Slowest day: 920ms
14-day trend: Stable0%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 3 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 Multivariate Test Analyzer Engine, so you can see the experience inside your AI.

It does not authenticate your account with Multivariate Test Analyzer Engine. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Multivariate Test Analyzer Engine Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_dnnmpTideBwOAAj381Y6Z0ks6utOj97h7UgsSaXa/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 — Multivariate Test Analyzer Engine capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "multivariate-test-analyzer-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_dnnmpTideBwOAAj381Y6Z0ks6utOj97h7UgsSaXa/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 Multivariate Test Analyzer Engine in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Multivariate Test Analyzer Engine owners hand off.

This MCP is built for professionals running complex experiments who need statistical rigor without manual math.

01
Conversion Rate Optimizers
They hand off multivariate test data to the AI to find winning variants.
02
Data Analysts
They use the capability to automate factorial design calculations and interaction checks.
03
Growth Product Managers
They rely on the AI to interpret experiment results and decide on winning configurations.

FAQ

Questions Multivariate Test Analyzer Engine owners ask.

01
What kind of experiments can I run with this MCP?
This MCP is designed for 2k Factorial Design of Experiments (DOE) and multivariate testing.
02
What data do I need to provide for calculations?
To calculate main effects, you must provide the levels, conversions, and visits for each combination in your experiment.
03
Can I detect if two elements work better together?
Yes, you can use the analyze_interaction_effects capability to detect dependencies between pairs of factors.
04
Which AI clients can I use with this MCP?
You can use this MCP with any compatible client like Claude, Cursor, Windsurf, or VS Code.
05
How does the MCP find the best test result?
The identify_winning_combination capability analyzes the data to pinpoint the statistically optimal configuration.
06
What is the purpose of calculate_main_effects?
This capability answers which individual elements or variants are driving the most significant change in conversions by calculating the average performance impact of switching a factor from its baseline to its variant level.
07
How can I detect if two elements work better together?
Use the analyze_interaction_effects capability. It compares the observed performance of two-factor combinations against the expected performance if they acted independently, identifying significant dependencies.
08
How do I find the best configuration for my test?
The identify_winning_combination capability identifies the specific setup with the highest conversion rate and verifies if it is statistically significantly better than the baseline using error variance analysis.

Explore

More in Statistics

Connect Multivariate Test Analyzer Engine 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.

01-03

Analyze interaction effects

Calculate main effects

Identify winning combination

What teams actually run

E-commerce UX Testing

Landing Page Optimization

Marketing Campaign Analysis

Feature Rollout Evaluation

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

Multivariate Test Analyzer Engine Connector

Claude Desktop

Conversion Rate Optimizers

Data Analysts

Growth Product Managers

What kind of experiments can I run with this MCP?

What data do I need to provide for calculations?

Can I detect if two elements work better together?

Which AI clients can I use with this MCP?

How does the MCP find the best test result?

What is the purpose of calculate_main_effects?

How can I detect if two elements work better together?

How do I find the best configuration for my test?

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