How to Use the Outlier Detection Engine MCP in LlamaIndex

Index clean, anomaly-free datasets into LlamaIndex vector stores by running local statistical checks first.

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Connect Outlier Detection Engine MCP to LlamaIndex

Create your Vinkius account to connect Outlier Detection Engine to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Outlier Detection Engine with LlamaIndex

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Index clean data into LlamaIndex

To clean your CSV and JSON files locally before converting them into LlamaIndex document nodes, use the `detect_outliers` tool. By filtering out statistical noise first, your search queries return highly accurate context.

Query past anomaly reports via RAG

Your `detect_outliers` tool output can be indexed directly into a queryable LlamaIndex vector store. Querying past runs of the tool tells you exactly which files had high variance last week without re-running any math.

Build an MCP Server data pipeline

Allowing your LlamaIndex pipelines to combine document parsing with deterministic math, the `detect_outliers` tool runs locally on your raw files. Your agent uses the tool to verify data integrity before any embeddings are generated, saving you API costs on useless vectors.

Setup guide

Set up Outlier Detection 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 Outlier Detection 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 Outlier Detection Engine tools.",
)
response = await agent.run("List recent Outlier Detection Engine data")

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Common questions about Outlier Detection Engine MCP in LlamaIndex

You load the tool using the MCP tool spec and pass it to your function agent. The agent executes `detect_outliers` to inspect local data frames before indexing them.

Yes, you can run the tool on source files before chunking. This ensures your vector store only contains clean, representative data points for semantic retrieval.

Initialize the basic MCP client with the server URL and convert it to a tool list. Your agent can then call `detect_outliers` as part of its ingestion loop.

The tool uses Z-Score and IQR to find statistical deviations. This provides a clear mathematical basis for why a row was excluded from your index.

Your local data files never leave your system. The tool processes the tables in an isolated local runtime, returning only the statistical indices without uploading raw data anywhere.

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