How to Use the Shipmondo MCP in AutoGen

Q: How can I use Shipmondo with AutoGen for rate negotiations?

You set up agents that call getshippingquote and then challenge the results. The agents debate optimal carrier choice, converging on a single best-fit option based on their programmed roles.

Q: If I use Shipmondo with AutoGen, what data types are involved in account management?

The server touches account balance records via getaccountbalance. Agents can debate whether current usage exceeds available funds before any shipment is approved.

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Connect Shipmondo MCP to AutoGen

Create your Vinkius account to connect Shipmondo 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 Shipmondo with AutoGen

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Perplexity

Negotiate shipment feasibility among agents.

Set up multiple agents: a 'Pricing Agent' calls `get_shipping_quote`, and an 'Inventory Agent' checks `list_warehouses`. They debate whether the quote is affordable given current stock levels. The final decision only proceeds if all agents agree. This consensus-driven loop is perfect for complex, multi-variable logistics decisions.

Coordinate shipment and balance checks.

One agent asks to `create_shipment`. A 'Risk Agent' immediately intercepts this by calling `get_account_balance` first. They argue about the budget, forcing a re-evaluation of the initial request until the necessary funds are confirmed. AutoGen lets you build systems where the answer requires internal negotiation between expert roles.

Resolve complex return processing.

A 'Logistics Agent' calls `list_returns`. A 'Compliance Agent' then uses that data to check if the items need special handling or documentation. They must agree on a single process before the final action is taken. This ensures no critical step—like customs requirements—is overlooked because multiple viewpoints are considered.

Setup guide

Set up Shipmondo 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 Shipmondo 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="Shipmondo_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Shipmondo. 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 Shipmondo MCP in AutoGen

You set up agents that call `get_shipping_quote` and then challenge the results. The agents debate optimal carrier choice, converging on a single best-fit option based on their programmed roles.

Yes. You can have one agent propose the shipment details and another agent verify compliance or capacity before allowing `create_shipment` to run. The system reaches consensus on the action.

The server touches account balance records via `get_account_balance`. Agents can debate whether current usage exceeds available funds before any shipment is approved.

You'll give one agent the goal of listing carriers, and another agent can cross-check that list against warehouse availability (`list_warehouses`) to provide a fully vetted answer.

The agents debate the best method for handling returned items, calling `list_returns` and cross-referencing it with rules until they agree on the final disposition steps.

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