Data Analysis Prover Connector for AI agents.
1 live capability
Ensure your data science reports are statistically sound and honest.
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Why people use Data Analysis Prover
Data Analysis Prover: Stop AI from making fake statistical claims
This Connector automates that scrutiny. It acts as a gatekeeper that forces your agent to justify every claim with actual statistical evidence. Instead of guessing if the AI's conclusion is valid, you get a structured reflection that proves it.
What Vinkius changes
That it turns your AI from a data summarizer into a rigorous statistical reviewer.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Verifying marketing campaign ROI
A manager asks the AI to prove a new email subject line increased sales.
- Real-world use case 02
Checking product feature retention
A product team wants to know if a new feature improved retention.
- Real-world use case 03
Summarizing skewed salary data
A researcher is analyzing salary data.
Complete set · 1capability
The complete Data Analysis Prover capability set.
These are the exact actions your AI can choose when you ask it to work with Data Analysis Prover.
01
1 capability in this set.
Part of 1 available through Data Analysis Prover.
- 01 Capability
Validate data analysis
Pass a statistical claim or research interpretation to the Connector to get a rigorous peer review of the methodology. It checks for sample validity, causal logic, and visual honesty to ensure your data is not misleading.
Set up in minutes
One URL. Then ask Data Analysis Prover to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Data Analysis Prover 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_z0UOTGMw5z9e0sSzQndQJ5ZX9DZMH1doH3kzey3a/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 Data Analysis Prover, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Data Analysis Prover for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_z0UOTGMw5z9e0sSzQndQJ5ZX9DZMH1doH3kzey3a/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 Data Analysis Prover URL.
- Step 03
Save and start
Save the connection and enable Data Analysis Prover in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"data-analysis-prover": {
"url": "https://edge.vinkius.com/vk_preview_z0UOTGMw5z9e0sSzQndQJ5ZX9DZMH1doH3kzey3a/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 Data Analysis Prover
Open Agent mode in chat and ask: "Using Data Analysis Prover, help me...". 1 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"data-analysis-prover": {
"url": "https://edge.vinkius.com/vk_preview_z0UOTGMw5z9e0sSzQndQJ5ZX9DZMH1doH3kzey3a/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 Data Analysis Prover
Ask Copilot: "Using Data Analysis Prover, help me...". 1 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"data-analysis-prover": {
"url": "https://edge.vinkius.com/vk_preview_z0UOTGMw5z9e0sSzQndQJ5ZX9DZMH1doH3kzey3a/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 Data Analysis Prover
Open Cascade and ask: "Using Data Analysis Prover, help me...". 1 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"data-analysis-prover": {
"url": "https://edge.vinkius.com/vk_preview_z0UOTGMw5z9e0sSzQndQJ5ZX9DZMH1doH3kzey3a/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 Data Analysis Prover
Ask Cline: "Using Data Analysis Prover, help me...". 1 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add data-analysis-prover --transport http "https://edge.vinkius.com/vk_preview_z0UOTGMw5z9e0sSzQndQJ5ZX9DZMH1doH3kzey3a/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 Data Analysis Prover
Ask Claude: "Using Data Analysis Prover, show me...". 1 tools are ready
Where the request belongs
Work Data Analysis Prover can move forward.
This is for data professionals who need to ensure their AI generated reports are bulletproof. It is built for anyone whose job involves presenting data to stakeholders who will notice a sloppy p-value or a misleading chart.
Marketing Analyst
Uses this to verify that a campaign actually drove sales rather than just happening at the same time.
Data Scientist
Uses this to quickly peer review AI generated research summaries for statistical rigor.
Business Intelligence Lead
Uses this to ensure that executive dashboards aren't using deceptive scales or biased samples.
Academic Researcher
Uses this to maintain high standards of data integrity when interpreting large scale experimental results.
When one Connector is not enough
Carry the request into a workflow.
Combine Data Analysis Prover with the systems that finish the task.
View all recipesHow 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
Connectors 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
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsA/B Test Significance Calculator
Calculate statistical significance, required sample sizes, and power for A/B tests.
Multivariate Test Analyzer
Perform 2k factorial analysis to identify optimal element combinations and interaction effects in multivariate experiments.
AB Test Sample Size Calculator
Calculate required sample size, test duration, and peeking risk for A/B experiments.
Test Duration Calculator
Calculate required A/B test duration, sample sizes, and experiment risk levels.
Chi-Square Test Engine
Run exact Chi-Square independence tests on contingency tables local. Get CPU-guaranteed chi² statistics and p-values for categorical analysis.
ANOVA Calculator Engine
Run exact One-Way ANOVA tests to compare means across multiple groups local. Get CPU-guaranteed F-scores and p-values, not LLM guesses.
Bring your own AI
Change the model, client or framework. Keep Data Analysis Prover 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 Data Analysis Prover.
The practical details behind the request, access and result.
Can the Data Analysis Prover catch misleading charts?
Yes. It audits your charts for common tricks like truncated Y-axes, dual scales, and distorted proportions to ensure your data remains honest.
Does Data Analysis Prover help with small sample sizes?
Yes. It flags Sample Blindness by forcing the AI to report the sample size (N) and conduct a power analysis to see if the results are actually reliable.
How does Data Analysis Prover handle p-values?
It prevents Significance Theater by requiring the AI to include effect sizes like Cohen's d alongside p-values, so you know if a result actually matters.
Can I use Data Analysis Prover for marketing research?
Absolutely. It is perfect for checking if marketing campaign results are statistically significant or just a result of random noise.
Will Data Analysis Prover make my AI more accurate?
It makes your AI's statistical claims more accurate by forcing it to follow rigorous data science methodologies instead of taking shortcuts.
Can it detect if a correlation is actually a cause?
Yes, it identifies Correlation Confusion by checking for confounders and distinguishing between observational associations and experimental evidence.
Why is p<0.05 not enough?
p-value measures probability, not magnitude. Cohen's d: 0.2=small, 0.5=medium, 0.8=large. A p<0.001 with d=0.05 is trivial. Report effect size + 95% CI + practical significance.
When can I say 'causes' vs 'associated with'?
Only RCTs establish causation. Observational studies show association. Control confounders, test reverse causality, check dose-response. Even then: 'associated with' unless experimental design.
Why is the mean misleading on skewed data?
Income example: mean $65K, median $45K. The mean is pulled by outliers. Right-skewed data: median represents 'typical' better. Test normality with Shapiro-Wilk before choosing parametric tests.
One connection away
Give your agent a direct line to Data Analysis Prover.
Connect Data Analysis Prover once. Keep it beside 6,100+ managed Connectors when the next task needs more.
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