Bring Statistics
to LlamaIndex
Learn how to connect Normality Test Engine to LlamaIndex 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 LlamaIndex?
LlamaIndex agents combine Normality Test Engine tool responses with indexed documents for comprehensive, grounded answers. Connect 1 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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Data-first architecture: LlamaIndex agents combine Normality Test Engine tool responses with indexed documents for comprehensive, grounded answers
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Query pipeline framework lets you chain Normality Test Engine tool calls with transformations, filters, and re-rankers in a typed pipeline
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Multi-source reasoning: agents can query Normality Test Engine, a vector store, and a SQL database in a single turn and synthesize results
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Observability integrations show exactly what Normality Test Engine tools were called, what data was returned, and how it influenced the final answer
Normality Test Engine in LlamaIndex
Normality Test Engine and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Normality Test Engine to LlamaIndex 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 LlamaIndex
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 LlamaIndex 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 LlamaIndex
Every tool call from LlamaIndex 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 LlamaIndex connect to MCP servers?
Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.
Can I combine MCP tools with vector stores?
Yes. LlamaIndex agents can query Normality Test Engine tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.
Does LlamaIndex support async MCP calls?
Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.
BasicMCPClient not found
Install: pip install llama-index-tools-mcp
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