How to Use the BLS JOLTS — Job Openings, Quits & Turnover MCP in AutoGen

Q: How does the BLS JOLTS — Job Openings, Quits & Turnover server handle complex series lookbacks?

The querybls tool accepts up to 50 concurrent series IDs. Your agents can build a single payload to pull wide-ranging historical data in one conversational turn.

Let your AutoGen agents debate labor market trends using live BLS JOLTS metrics.

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Setup BLS JOLTS — Job Openings, Quits & Turnover with AutoGen

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Resolve agent debates with real JOLTS data

The `get_jolts_data` tool provides the ground-truth metrics needed to settle arguments between your agents. When an analyst agent disagrees with an economist agent about quit rates, they call this tool to inspect the official numbers. AutoGen passes the raw statistics directly into the conversation thread. This ensures the entire multi-agent group bases its final consensus on verified national metrics.

Run bulk series lookbacks via the MCP Server

The `query_bls` tool allows your agents to run up to 50 concurrent lookbacks using explicit BLS series IDs. A specialized researcher agent can fetch these complex timeseries in one turn. After receiving the data, the researcher agent shares it with the rest of the group. This parallel execution keeps your multi-agent conversation fast and efficient.

Automate complex economic analysis pipelines

Your agents coordinate to execute `get_jolts_data` and interpret the resulting hire-and-fire trends. One agent fetches the raw figures while another writes the summary report. This division of labor happens autonomously based on the tools available. You do not have to write manual routing rules to pass the data between agents.

Setup guide

Set up BLS JOLTS — Job Openings, Quits & Turnover 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 JOLTS — Job Openings, Quits & Turnover 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 JOLTS — Job Openings, Quits & Turnover_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent BLS JOLTS — Job Openings, Quits & Turnover 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 JOLTS — Job Openings, Quits & Turnover_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent BLS JOLTS — Job Openings, Quits & Turnover data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about BLS JOLTS — Job Openings, Quits & Turnover MCP in AutoGen

Install the autogen-ext package and use the StreamableHttpServerParams to point to your Vinkius endpoint. Pass the resulting tool list directly to your AssistantAgent.

Yes. The adapter converts the tools into schemas that any agent in your conversation group can call when they need labor metrics.

The `query_bls` tool accepts up to 50 concurrent series IDs. Your agents can build a single payload to pull wide-ranging historical data in one conversational turn.

No. The McpToolAdapter handles the underlying schema conversion automatically. You only need to provide the HTTP connection URL from Vinkius.

All queries pass through a secure V8 isolate sandbox on Vinkius. Only public BLS series codes and JOLTS metrics are processed, meaning your MCP Server session remains completely private.

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