How to Use the Normality Test Engine MCP in LlamaIndex

Index the statistical properties of your data. Let your LlamaIndex agent query which datasets are safe for parametric tests.

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Connect Normality Test Engine MCP to LlamaIndex

Create your Vinkius account to connect Normality Test Engine to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Normality Test Engine with LlamaIndex

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Build a Statistical Knowledge Base

Your LlamaIndex agent uses the `test_normality` tool to get the skewness and kurtosis for any dataset. It’s a fast, local calculation that sidesteps LLM math errors. Here’s the thing: LlamaIndex doesn't just use the result once. It indexes the output—the dataset's identifier, its skewness, and its kurtosis—into a vector store. You're not just running a test; you're creating a permanent, queryable record of your data's statistical shape.

Ground Your Agent in Hard Numbers

This is how you build a RAG system that understands data quality. When you ask your agent about your experiments, it performs a semantic search over the indexed results from `test_normality`. You can ask, "Which datasets from the Q3 trials were highly skewed?" Your agent won't guess. It will retrieve the specific, historical results from past tool calls, giving you answers grounded in actual math.

Queryable MCP Server Results

Stop re-running the same validation scripts. This MCP server turns a one-time check into a durable asset for your team. Instead of digging through old notebooks, a new team member can just ask the agent, "Was the user signup data from last month normally distributed?" The agent queries its knowledge base of past `test_normality` runs and gives a direct answer. It's institutional memory for your data science workflow.

Setup guide

Set up Normality Test Engine MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Normality Test Engine MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Normality Test Engine tools.",
)
response = await agent.run("List recent Normality Test Engine data")

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Common questions about Normality Test Engine MCP in LlamaIndex

It feeds your RAG system a new kind of knowledge: statistical metadata. Your LlamaIndex agent can then answer questions about data quality by retrieving facts from past `test_normality` runs, making its responses more accurate and grounded in data.

Yes. Your LlamaIndex agent's logic determines when to call the tool. You can program it to only run the normality test on datasets that match certain criteria, like those intended for regression modeling.

Because LlamaIndex makes the results queryable with natural language. Instead of manually parsing a log file, you can simply ask your agent, "Find experiments with a kurtosis below 2.0." The Normality Test Engine provides the data; LlamaIndex makes it accessible.

No, and that's a key feature. The Normality Test Engine calculates the statistics, and only the results (skewness, kurtosis) and any metadata you provide get indexed by LlamaIndex. The raw data points are not stored, which is better for privacy and efficiency.

The server receives a numeric array to perform the calculation. It operates in a zero-trust environment on Vinkius. Each call happens in a new, isolated container that is immediately destroyed after sending the response, ensuring your data is never stored or logged.

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