How to Use the Troops MCP in AutoGen

Q: Can AutoGen manage job offers using Troops?

Yes. You can set up agents to constantly monitor listjobs. If a critical status change occurs, the debating team flags it and determines if manual intervention or an automated fix is needed.

Q: What kind of documentation flow does Troops and AutoGen support?

The system can debate whether to generate a contract (generatecontract) or if existing records are sufficient. The final, agreed-upon action—like calling signcontract—is what gets executed.

Q: Is tracking timesheets reliable with Troops and AutoGen?

The agents can monitor time entries by running submittimesheet. If the data is flagged as incomplete or contradictory, the debate cycle forces a review before the record is finalized.

Get consensus on HR decisions using multi-agent debate with the Troops MCP Server in AutoGen.

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Create your Vinkius account to connect Troops 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 Troops with AutoGen

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Run hiring debates across multiple agents via the Troops MCP Server.

You can set up a team of agents to manage recruitment. One agent might call `create_job` while another challenges the job description's scope, forcing a consensus on requirements before it moves forward. This makes your system robust because the final decision isn't just based on one tool output; it's agreed upon after debate.

Ensure compliance during contracting with AutoGen and MCP Server.

Need to generate a contract? Have Agent A call `generate_contract`, but let Agent B (the Compliance Officer) review the output first. Only when both agents agree does the system proceed to `sign_contract`. This debate mechanism prevents errors, ensuring that every contractual step is vetted by competing perspectives.

Automate full HR lifecycle processes in AutoGen.

The multi-agent system can handle the whole flow: Agent A uses `create_candidate`, and Agent B reviews their profile using `get_candidate`. If approved, a third agent calls `submit_timesheet` to start tracking hours. It’s not just automation; it's orchestrated consensus that drives the outcome.

Setup guide

Set up Troops 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 Troops 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="Troops_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

The agents debate the best path for candidates. They might compare a candidate's profile (`get_candidate`) against existing open roles (`list_jobs`), achieving consensus on next steps before any action is taken.

Yes. You can set up agents to constantly monitor `list_jobs`. If a critical status change occurs, the debating team flags it and determines if manual intervention or an automated fix is needed.

The system can debate whether to generate a contract (`generate_contract`) or if existing records are sufficient. The final, agreed-upon action—like calling `sign_contract`—is what gets executed.

The agents can monitor time entries by running `submit_timesheet`. If the data is flagged as incomplete or contradictory, the debate cycle forces a review before the record is finalized.

The Troops MCP Server manages sensitive employment records, specifically candidate personal details and timesheet submissions.

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