How to Use the BLS Wages — OEWS Occupational Employment MCP in AutoGen

Q: Can multiple AutoGen agents use BLS Wages — OEWS Occupational Employment simultaneously?

Yes, your agents can share access to the querybls tool. The server handles up to 50 concurrent lookbacks, allowing different agents to query regional wage data at the same time without hitting rate limits.

Q: How do AutoGen agents negotiate salary ranges using BLS Wages — OEWS Occupational Employment?

One agent fetches market data via querybls using specific series IDs. It then shares these median wage figures with a budgeting agent, who evaluates the numbers against internal company constraints to reach a consensus.

Let your AutoGen agents debate and verify market rate data using live BLS wage indices.

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Setup BLS Wages — OEWS Occupational Employment with AutoGen

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Resolve agent debates with verifiable BLS salary figures

The `query_bls` tool provides a single source of truth for your multi-agent AutoGen conversations. When a recruiting agent and a finance agent disagree on a salary range, they can call this tool to fetch actual market data. This consensus-driven approach ensures your system does not rely on arbitrary guesses. The agents use the raw timeseries output to negotiate and settle on realistic, data-backed salary offers.

Run high-throughput lookups using this MCP Server

This MCP Server allows your AutoGen agents to execute up to 50 concurrent lookbacks for different occupational codes. A coordination agent can split a massive hiring plan into individual queries and distribute them to specialized sub-agents. Each agent queries its assigned series ID using `query_bls` and reports the median wage back to the group. This parallel execution keeps your multi-agent workflows fast and responsive.

Automate complex HR audits using multi-agent workflows

AutoGen developers can build dedicated audit teams where one agent flags compliance risks and another fetches federal wage benchmarks. The auditor agent uses `query_bls` to pull exact state-level earnings and compares them to internal payroll records. If a discrepancy is found, the agents negotiate a resolution before presenting the final report to you. You get automated, highly accurate compliance checks without manual data entry.

Setup guide

Set up BLS Wages — OEWS Occupational Employment 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 BLS Wages — OEWS Occupational Employment 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="BLS Wages — OEWS Occupational Employment_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent BLS Wages — OEWS Occupational Employment 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="BLS Wages — OEWS Occupational Employment_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent BLS Wages — OEWS Occupational Employment data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about BLS Wages — OEWS Occupational Employment MCP in AutoGen

Yes, your agents can share access to the `query_bls` tool. The server handles up to 50 concurrent lookbacks, allowing different agents to query regional wage data at the same time without hitting rate limits.

One agent fetches market data via `query_bls` using specific series IDs. It then shares these median wage figures with a budgeting agent, who evaluates the numbers against internal company constraints to reach a consensus.

No, the McpToolAdapter in AutoGen automatically handles the schema conversion. Your agents receive structured JSON wage data directly from the `query_bls` tool, making it immediately usable in agent conversations.

Yes, you can connect AutoGen to this server via our secure Vinkius endpoint. This eliminates local configuration headaches while giving your multi-agent system instant access to federal wage statistics.

No, your internal payroll details remain strictly within your local AutoGen environment. This MCP Server only receives public BLS series IDs to pull federal wage data, keeping your sensitive corporate financial records completely isolated.

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