How to Use the DCL Logistics MCP in AutoGen

Deploy multi-agent teams in AutoGen to debate DCL Logistics fulfillment tasks. Let your agents negotiate the best shipping strategy.

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Connect DCL Logistics MCP to AutoGen

Create your Vinkius account to connect DCL Logistics 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 DCL Logistics with AutoGen

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Enable agent debate on fulfillment

Set up specialized agents to handle your DCL Logistics tasks. One agent can call `list_fulfillment_orders` while another challenges the fulfillment priority. This consensus-driven approach ensures your team reaches the right conclusion. They work together to resolve complex order issues through active deliberation.

Automate shipment status negotiation

Your agents can discuss carrier updates using `get_shipment_details`. If a delivery is delayed, they debate the best way to notify the customer. This prevents single-agent errors. You get a reliable system where multiple perspectives weigh in before taking action on your shipment data.

Coordinate warehouse stock movements

Use `list_low_stock_items` to trigger a conversation between your agents. A logistics agent might suggest a restock, while a finance agent checks the budget. They negotiate the outcome based on the live data they retrieve. This results in smarter, more deliberate management of your warehouse inventory.

Setup guide

Set up DCL Logistics 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 DCL Logistics 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="DCL Logistics_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent DCL Logistics 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="DCL Logistics_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent DCL Logistics 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 DCL Logistics. 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 DCL Logistics MCP in AutoGen

You pass the tool list directly to the AssistantAgent constructor. The adapter handles schema conversion so your agents can call tools immediately.

They are designed for exactly that. Each agent can call the tools and challenge the findings of others to reach a consensus.

It supports both stdio and Streamable HTTP. Choose the transport that fits your local or cloud-based agent deployment.

It is straightforward. You install the extension, define your server parameters, and the agents gain access to the full toolset.

Access is restricted to your specific endpoint token. All return records and customer identifiers are scoped within your private session memory.

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