How to Use the Feature Scaler Engine MCP in LlamaIndex

Add precise feature normalization to LlamaIndex workflows with the Feature Scaler Engine MCP Server.

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Connect Feature Scaler Engine MCP to LlamaIndex

Create your Vinkius account to connect Feature Scaler 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 Feature Scaler Engine with LlamaIndex

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Index normalized features in LlamaIndex

You can now push scaled values directly into your vector store. The `scale_features` tool ensures that your indexed knowledge base stays consistent. LlamaIndex treats the output of this MCP Server as a grounded data source. You query past scaling configurations and get results based on the actual math performed.

Mathematical grounding for RAG

RAG applications rely on accurate vector representations. By using this tool, you standardize your input data so the index reflects real-world patterns. Your agent uses the tool to prepare documents or metrics before they get converted into embeddings. This prevents bias in your search results.

Filterable tool access

Control which parts of your data get scaled by using LlamaIndex's tool filtering. You restrict the Feature Scaler Engine to specific pipelines. This keeps your agent focused on the relevant numeric tasks. You only call the scaling logic when the situation demands it.

Setup guide

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

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Common questions about Feature Scaler Engine MCP in LlamaIndex

Install the llama-index-tools-mcp package and initialize a BasicMCPClient. Convert the tools into a list and pass them to your FunctionAgent.

It normalizes your input data, which leads to more uniform distance calculations. This makes your vector similarity searches more reliable.

It is built for that exact purpose. You scale your data, index it, and then query it to get grounded answers based on the normalized values.

Yes. The tool list generation supports async calls, fitting naturally into your non-blocking agent architecture.

The server runs as a local process, meaning your numeric data never touches the cloud. It only computes the Z-Score or MinMax math on your local machine.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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