How to Use the Square MCP in AutoGen

Q: What happens when AutoGen debates payment processing using Square?

One agent calls createpayment, and a second agent immediately checks the outcome with getpaymentdetails. They debate if the final result is confirmed, forcing the system to wait for explicit confirmation before moving on.

Q: Can AutoGen handle multi-step inventory reconciliation across Square?

Yes. You set up a process where one agent calls liststorelocations, and another agent uses those results to call getstockcount for every location, debating the final required count.

Q: Is AutoGen suitable for researching customer history using Square?

It's perfect. One agent searches with searchcustomers, and another challenges the result by running getcustomer to verify credentials or details, ensuring data integrity.

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Connect Square MCP to AutoGen

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Debating Payment Status

The `create_payment` tool starts the conversation. You can set up a debate between two agents: one that executes payment, and another that critiques it using `get_payment_details`. They argue over the success status until consensus is reached.

Analyzing Order Requirements

One agent calls `search_orders` to get initial data. A second agent reviews this against `list_catalog` to check for item availability. This negotiation process forces a decision on whether the order can actually be fulfilled.

Resolving Customer Conflicts

Use `search_customers` to pull initial records. A third agent then uses `get_customer` to verify the details against location-specific data from `list_store_locations`. The agents must agree on the correct customer profile before proceeding.

Setup guide

Set up Square 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 Square 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="Square_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

One agent calls `create_payment`, and a second agent immediately checks the outcome with `get_payment_details`. They debate if the final result is confirmed, forcing the system to wait for explicit confirmation before moving on.

Yes. You set up a process where one agent calls `list_store_locations`, and another agent uses those results to call `get_stock_count` for every location, debating the final required count.

Agents don't just run a single tool. They can be configured to pass data from `list_payments` into an agent that then calls `search_orders`, simulating a complex, multi-step investigation.

It's perfect. One agent searches with `search_customers`, and another challenges the result by running `get_customer` to verify credentials or details, ensuring data integrity.

The server touches customer names, payment records, and location IDs. With AutoGen, you must assign a 'Compliance Agent' to review every piece of PII before any final action is taken.

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