How to Use the CartonCloud MCP in AutoGen
Deploy AutoGen agents to debate and resolve CartonCloud logistics and shipping discrepancies.
Works with every AI agent you already use
…and any MCP-compatible client
Connect CartonCloud MCP to AutoGen
Create your Vinkius account to connect CartonCloud 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.
Let AutoGen agents audit your CartonCloud stock
This MCP server allows your multi-agent teams to inspect and verify physical stock counts collaboratively. One agent can call `get_product_stock` to check current inventory, while another pulls pending orders using `list_sale_orders` to debate whether you have enough stock to fulfill them. This collaborative auditing ensures that you don't overcommit inventory. The agents negotiate back and forth, using real data from `list_warehouse_products` to reach a consensus before updating your team.
Resolve consignment and shipping disputes
This toolset handles delivery discrepancies by letting specialized agents analyze transport logs. Your performance agent calls `list_consignments` to check delivery times, while your customer service agent pulls customer files with `list_logistics_customers`. They debate the cause of a shipping delay, analyzing transport details to find the bottleneck. Once they agree on where the breakdown occurred, they present a single, agreed-upon resolution to your operator.
Match purchase orders to invoices automatically
This CartonCloud MCP Server helps you automate complex financial reconciliation. Your billing agent calls `list_logistics_invoices` while your purchasing agent calls `list_purchase_orders` to cross-reference incoming goods against billed amounts. If the numbers don't match, the agents flag the mismatch and debate the likely cause based on the historical data in `get_sale_order_details`. You get a clean summary of the discrepancy without manually comparing spreadsheets.
Set up CartonCloud 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 includesmcp_server_toolsfor 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
AssistantAgentand callagent.run(). The agent invokes CartonCloud tools and returns structured results.
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="CartonCloud_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
tools=tools,
)
result = await agent.run("List recent CartonCloud data")
print(result.messages[-1].content) 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.
McpWorkbenchis 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=workbenchto your agent. State is preserved across multiple tool calls within the same session.
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="CartonCloud_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
workbench=workbench,
)
result = await agent.run("List recent CartonCloud data")
print(result.messages[-1].content) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by CartonCloud. 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 CartonCloud MCP in AutoGen
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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