How to Use the Deputy MCP in AutoGen

Q: How do I set up Deputy with AutoGen?

Install the MCP extension for AutoGen and use the mcpservertools helper. Pass the resulting tool list to your AssistantAgent constructor.

Enable AutoGen agents to debate and negotiate shift assignments using live Deputy data from this MCP Server.

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Setup Deputy with AutoGen

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Perplexity

AutoGen agents debate Deputy shifts

Give your agents `list_currently_active_shifts` and watch them argue about staffing needs. One agent pushes for efficiency while another flags overtime costs. They reach consensus before taking action. You get a deliberated decision rather than a single, uninformed API call.

Cross-check Deputy timesheets

Assign the timesheet review task to a specialized agent using `list_completed_timesheets`. It checks the records against your constraints. If the data looks wrong, the agent asks another peer to verify. This multi-agent loop catches errors that a simple script would miss.

Negotiate leave in AutoGen

Use `list_pending_leave_approvals` as the input for a negotiation agent. It discusses the impact on the roster with your management agents. They decide whether to approve based on current business load. It removes the bottleneck of manual scheduling decisions.

Setup guide

Set up Deputy 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 Deputy 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="Deputy_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about Deputy MCP in AutoGen

Yes. By passing the Deputy tools to your agents, they exchange messages about shift data to reach a group decision.

Install the MCP extension for AutoGen and use the mcp_server_tools helper. Pass the resulting tool list to your AssistantAgent constructor.

You can assign tools to specific agents. This allows you to create a secure hierarchy where only certain agents can modify Deputy data.

Your employee records are handled within an ephemeral Vinkius sandbox. No data persists beyond the scope of your agent's conversation.

The agent reports the error in the chat history. You can see the exact tool call failure and decide how the team should pivot.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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