How to Use the ApparelMagic MCP in AutoGen

Deploy AutoGen agent teams to debate and resolve ApparelMagic inventory and shipping discrepancies.

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Connect ApparelMagic MCP to AutoGen

Create your Vinkius account to connect ApparelMagic 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 ApparelMagic with AutoGen

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Resolve shipping conflicts with an AutoGen MCP Server

This ApparelMagic MCP Server lets your AutoGen agents work together to find the root cause of shipping conflicts. When a shipment goes missing, manual tracking is a bottleneck. One agent pulls shipping details with `get_shipment` while another checks the original order via `get_order`. They compare the data, flag discrepancies in delivery dates, and negotiate the best resolution. You get a clear, agreed-upon action plan without having to dig through multiple ERP screens yourself.

Coordinate stock levels across warehouse teams

This ApparelMagic integration keeps your sales and warehouse teams on the same page. An inventory agent can continuously run `list_inventory` while a sales agent monitors `list_orders` for sudden spikes. When a style runs low, the agents debate whether to transfer stock or trigger a new production run. They use `list_styles` to verify production lead times before presenting the final decision to your team.

Manage customer accounts with AutoGen agents

This ApparelMagic toolset automates complex customer audits using conversational agents. A billing agent can fetch customer profiles using `list_customers` while a logistics agent pulls their entire order history with `list_orders`. The agents cross-reference the data to identify accounts with outstanding balances or delayed shipments. They compile a report using `get_customer` and present a prioritized list of accounts that need immediate attention.

Setup guide

Set up ApparelMagic 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 ApparelMagic 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="ApparelMagic_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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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="ApparelMagic_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ApparelMagic. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about ApparelMagic MCP in AutoGen

Use the MCP server tools adapter to load the tools from your Vinkius MCP endpoint. You can then pass the resulting tool list directly to your AssistantAgent constructor for automatic schema conversion.

Yes. You can share the tool list across multiple agents in your group chat. This allows a logistics agent to call `list_shipments` while a sales agent calls `list_orders` in the same conversation.

Yes. The MCP server provides the raw data that your agents need to debate. For example, agents can argue over stock reallocation based on real-time numbers fetched from `list_inventory`.

The server supports both standard I/O and streamable HTTP transports. This ensures you can run your agent cluster locally or deploy it to a cloud environment without changing your tool configuration.

Absolutely. Your warehouse stock counts and shipment tracking details are processed entirely in memory. Vinkius runs the MCP server in an ephemeral sandbox with token-based authentication, ensuring your ERP data is never exposed or logged.

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