How to Use the Feature Scaler Engine MCP in AutoGen

Enable multi-agent debate on data normalization using the Feature Scaler Engine MCP Server.

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Connect Feature Scaler Engine MCP to AutoGen

Create your Vinkius account to connect Feature Scaler Engine to AutoGen 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 AutoGen

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Multi-agent scaling decisions

Your agents discuss whether to use Z-Score or MinMax scaling for a specific dataset. The `scale_features` tool provides the execution backend for their consensus. One agent proposes the normalization method, while another checks for potential data distribution issues. They agree on a strategy before running the numbers.

Automated schema conversion

The McpToolAdapter handles the handshake between your AutoGen agents and the server. You don't write glue code for the schema. Everything happens via the standard MCP protocol. Your agents see the tool as a native function and call it during their deliberations.

Consensus-driven preprocessing

You build systems where agents negotiate the best way to handle outliers. They use the Feature Scaler Engine to test different scaling approaches. This ensures the final output meets the requirements defined by your entire agent team. It removes the guesswork from your data preparation.

Setup guide

Set up Feature Scaler Engine MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Feature Scaler Engine tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Feature Scaler Engine_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Feature Scaler Engine data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Feature Scaler Engine_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Feature Scaler Engine data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about Feature Scaler Engine MCP in AutoGen

Use the autogen-ext package to register the MCP server tools. Pass the tool list into your AssistantAgent so the team can access the scaling logic.

They can. You define the agents with different priorities, and they will use the tool to evaluate the data and reach a collective decision.

The server manages multiple tool calls sequentially within its local sandbox. It is built to handle requests from your agent team as they arrive.

The tool adapter makes it trivial. You point the configuration to the server URL, and the agents handle the rest via their internal conversation loops.

The server operates locally, so no raw numeric data is transmitted over the network. Your agent team processes information entirely within your controlled infrastructure.

Use it with your favorite AI tools

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