How to Use the Deel MCP in AutoGen

Q: Can AutoGen agents disagree on Deel data?

That is the point. By debating the contents of listpeople or listcontracts, they surface potential issues that a single agent might overlook.

Debate HR policies with AutoGen agents. Connect this MCP server to enable consensus-driven decisions on Deel contracts and tasks.

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Connect Deel MCP to AutoGen

Create your Vinkius account to connect Deel 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 Deel with AutoGen

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Negotiate Deel tasks with AutoGen

Assign one agent to `list_tasks` and another to `list_time_off`. They compare the data and decide if a project timeline needs adjustment. This conflict resolution happens in real-time. Your agents reach a consensus before suggesting a final action to you.

Validate Deel changes with multi-agent debate

Let a security agent check `list_invoices` while a finance agent reviews the payment status. They debate the validity of each record until they agree. This prevents errors caused by single-agent bias. You get a verified decision backed by multiple perspectives on your HR data.

Automate Deel operations via agents

The McpToolAdapter handles all schema conversion for your AutoGen agents automatically. They communicate with the server to pull contract metadata and resolve discrepancies. Your system runs without constant oversight. The agents handle the back-and-forth required to manage complex HR workflows.

Setup guide

Set up Deel 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 Deel 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="Deel_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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Common questions about Deel MCP in AutoGen

You pass the tool list to the AssistantAgent constructor. The agents then call these tools during their conversation to gather the information they need.

That is the point. By debating the contents of `list_people` or `list_contracts`, they surface potential issues that a single agent might overlook.

Yes, the framework supports Streamable HTTP transports. Your agents receive updates from the server as they happen during their deliberation.

You simply add more agents to the group chat. Each new agent can access the MCP tools to contribute its own analysis to the collective decision.

The data is handled within the memory space of your local agents. We use zero-trust infrastructure to ensure your personal HR records are never cached or exposed.

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