How to Use the DOL (Department of Labor) MCP in AutoGen

Q: How do AutoGen agents share DOL (Department of Labor) tool outputs?

When an agent calls getwhdcompliance, the raw JSON payload is converted into a standard format that all other agents in the conversation group can read and debate.

Q: Can I limit which AutoGen agents call the DOL (Department of Labor) MCP Server?

Yes. You register the tools like getoshainspections only with the specific investigator agents that require access, keeping your other conversational agents focused on analysis.

Q: How does AutoGen convert schemas for the DOL (Department of Labor) server?

The McpToolAdapter handles schema conversion automatically. It translates the tool definitions for getetadata into the exact format that AutoGen's AssistantAgent expects.

Let AutoGen agents debate DOL (Department of Labor) compliance data to reach objective, risk-mitigated decisions.

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Setup DOL (Department of Labor) with AutoGen

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AutoGen multi-agent debates on labor compliance

Set up a multi-agent system where one agent pulls wage records using `get_whd_compliance` and another checks safety histories with `get_osha_inspections`. The agents then debate whether a contractor meets your internal compliance standards. This consensus-driven approach stops simple errors. One agent might flag a minor infraction, but the coordinator agent can weigh it against the broader compliance record before making a final recommendation.

Mine safety risk assessments

Deploy specialized agents to analyze industrial risks. Your safety agent calls `get_msha_inspections` to find active mines, while your compliance agent queries `get_msha_violations` to analyze the severity of past penalties. They cross-reference their findings to calculate a combined risk score. This multi-agent deliberation ensures that physical safety hazards and administrative violations are both factored into the final report.

Automated training program audits

Run structured evaluations on regional training initiatives. One agent pulls workforce development metrics using `get_eta_data`, while a separate analyst agent evaluates how these numbers align with local labor demands. By dividing the retrieval and analysis tasks between distinct agents, you get objective, highly focused assessments of federal employment programs without manual intervention.

Setup guide

Set up DOL (Department of Labor) 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 DOL (Department of Labor) 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="DOL (Department of Labor)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent DOL (Department of Labor) 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="DOL (Department of Labor)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent DOL (Department of Labor) data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by DOL (Department of Labor). 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 DOL (Department of Labor) MCP in AutoGen

When an agent calls `get_whd_compliance`, the raw JSON payload is converted into a standard format that all other agents in the conversation group can read and debate.

Yes. You register the tools like `get_osha_inspections` only with the specific investigator agents that require access, keeping your other conversational agents focused on analysis.

The `McpToolAdapter` handles schema conversion automatically. It translates the tool definitions for `get_eta_data` into the exact format that AutoGen's `AssistantAgent` expects.

This integration supports both stdio and Streamable HTTP transports, allowing your agents to communicate with the federal endpoints over secure, low-latency connections.

All requests for wage theft audits and physical safety inspections are handled through ephemeral V8 isolates. Your queries are processed in memory and wiped immediately, leaving no trace of the inspected entities on Vinkius.

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