How to Use the Veeqo MCP in AutoGen

Q: Can I use AutoGen for complex order processing with Veeqo?

You can. For example, one agent verifies stock via listinventoryproducts, another confirms the customer exists via listcustomers, and a third reviews the complete details from getorderdetails before any action is taken.

Q: How do I check shipment status using AutoGen and Veeqo?

You can deploy a debate where one agent checks listshipments and another validates that the corresponding order exists via getorderdetails. The agents must agree on the current status.

Achieve consensus on fulfillment using AutoGen with Veeqo's multi-agent debate framework.

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Connect Veeqo MCP to AutoGen

Create your Vinkius account to connect Veeqo 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 Veeqo with AutoGen

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Simulated Order Fulfillment Debate

A group of agents can discuss creating an order. One agent calls `get_product_details` to check pricing, while another calls `list_inventory_products` for stock levels. The system debates the findings until consensus is reached on whether or how to proceed with calling `create_manual_order`.

Multi-Step Shipment Planning

You can set up agents to negotiate shipping. One agent calls `list_shipments`, and a second agent uses that data, combined with customer records from `list_customers`, to determine the optimal carrier. This process mimics human planning where different viewpoints challenge assumptions.

Comprehensive Customer Review

Agents can collaborate using `list_orders` and `get_order_details`. One agent might flag an order that is missing shipment data, while another confirms the customer's address via `list_customers`. The final decision requires consensus on all necessary steps before proceeding.

Setup guide

Set up Veeqo 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 Veeqo 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="Veeqo_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

AutoGen uses the MCP tools to simulate deliberation. Instead of executing a single workflow, multiple agents debate the best course of action by calling different Veeqo functions and challenging each other's results.

You can. For example, one agent verifies stock via `list_inventory_products`, another confirms the customer exists via `list_customers`, and a third reviews the complete details from `get_order_details` before any action is taken.

Yes. The framework's core function is consensus-driven decision making. You build systems where the final output isn't obvious and requires multiple agents to argue toward a conclusion.

The server handles customer records, including specific order details accessed via `get_order_details` and product information from `get_product_details`. You must manage access control for this sensitive data.

You can deploy a debate where one agent checks `list_shipments` and another validates that the corresponding order exists via `get_order_details`. The agents must agree on the current status.

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