Bring Statistics
to Pydantic AI
Learn how to connect Normality Test Engine to Pydantic AI and start using 1 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Normality Test Engine MCP Server?
Before running t-tests, ANOVA, or linear regression, you need to verify that your data is normally distributed. LLMs cannot eyeball a distribution from raw numbers — they will guess and often guess wrong.
This MCP uses simple-statistics to compute exact Skewness and Kurtosis coefficients, then applies a Jarque-Bera test to determine normality. The AI gets a definitive pass/fail verdict with the exact test statistic and p-value.
The Superpowers
- Zero Hallucination: Exact statistical coefficients computed locally.
- Automated Verdict: Returns a clear 'normal' or 'not normal' interpretation.
- Descriptive Statistics: Also provides exact Mean, Std Dev, Skewness, and Kurtosis.
- Data Privacy: Your research data stays entirely on your local machine.
Built-in capabilities (1)
Perform an exact deterministic Jarque-Bera normality test on numeric data without LLM math hallucinations
Why Pydantic AI?
Pydantic AI validates every Normality Test Engine tool response against typed schemas, catching data inconsistencies at build time. Connect 1 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
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Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Normality Test Engine integration code
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Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your Normality Test Engine connection logic from agent behavior for testable, maintainable code
Normality Test Engine in Pydantic AI
Normality Test Engine and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Normality Test Engine to Pydantic AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Normality Test Engine in Pydantic AI
The Normality Test Engine MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 1 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Pydantic AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
How Vinkius secures
Normality Test Engine for Pydantic AI
Every tool call from Pydantic AI to the Normality Test Engine MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Is this the Shapiro-Wilk test?
This engine implements the Jarque-Bera normality test, which uses Skewness and Kurtosis. It is highly effective for medium-to-large samples and avoids the Shapiro-Wilk implementation gaps in JavaScript.
How many data points do I need?
The Jarque-Bera test works best with 30 or more samples. For very small samples (n < 20), consider using visual QQ-plot analysis as a complement.
What does a 'not normal' result mean for my analysis?
If your data is not normally distributed, parametric tests like t-tests and ANOVA may be unreliable. Consider using non-parametric alternatives like Spearman correlation or Mann-Whitney U tests.
How does Pydantic AI discover MCP tools?
Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.
Does Pydantic AI validate MCP tool responses?
Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
Can I switch LLM providers without changing MCP code?
Absolutely. Pydantic AI abstracts the model layer. your Normality Test Engine MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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