How to Use the DPD MCP in AutoGen

Q: Can AutoGen agents coordinate DPD returns?

Yes, a support agent can track a package using getshipmentstatus and, if a return is requested, coordinate with a logistics agent to find a drop-off point via findparcelshop.

Q: How do AutoGen agents resolve DPD API errors?

When an endpoint like getlabels returns an error, the agents discuss the failure in their chat history. They can decide to modify the shipment details and retry createshipment automatically.

Let your AutoGen agents debate and coordinate DPD fulfillment tasks in multi-agent conversations.

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Coordinate DPD dispatch via AutoGen agent debates

This MCP server enables multi-agent negotiation over shipping decisions. Your fulfillment agent drafts a package delivery using `create_shipment`, while a budget agent analyzes `list_products` to check if a cheaper service is available. They debate the trade-offs in your chat thread. Once they reach a consensus on cost and speed, the execution agent calls `get_labels` to finalize the paperwork and print the physical label.

Automated exception handling and cancellations

This logistics toolset lets a tracking agent use `get_parcel_status` to diagnose transit issues when a delivery delay is flagged. It passes the status to a customer relations agent, which decides whether to issue a refund or cancel the package. If cancellation is the agreed route, the system invokes `cancel_shipment` automatically. This collaborative loop resolves shipping bottlenecks without requiring developer intervention.

Dynamic geographic routing

Your international shipping agent queries `list_countries` to verify customs requirements for a destination. Simultaneously, a local routing agent uses `find_parcelshop` to locate regional drop-off points. The agents compile these inputs to construct the final shipping manifest via `get_manifest`. This multi-agent verification ensures that your international consignments comply with local DPD rules before dispatch.

Setup guide

Set up DPD 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 DPD 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="DPD_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about DPD MCP in AutoGen

You register the MCP Server tools with your AutoGen agent configuration using the MCP tool adapter. This exposes actions like `create_shipment` directly to the agents' conversation space.

Yes, a support agent can track a package using `get_shipment_status` and, if a return is requested, coordinate with a logistics agent to find a drop-off point via `find_parcelshop`.

When an endpoint like `get_labels` returns an error, the agents discuss the failure in their chat history. They can decide to modify the shipment details and retry `create_shipment` automatically.

Yes, you can filter the tool list before passing it to your AutoGen assistant. This ensures a billing agent can only view `list_products` while a warehouse agent handles `create_shipment`.

All recipient addresses and parcel tracking numbers are processed in isolated, ephemeral runtimes. The MCP Server ensures that your sensitive logistics payloads are never logged or stored.

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