How to Use the BLS JOLTS — Job Openings, Quits & Turnover MCP in OpenAI Agents SDK

Q: Can the OpenAI Agents SDK process BLS JOLTS — Job Openings, Quits & Turnover timeseries?

Yes, you use the querybls tool with your specific series IDs. It returns the raw numerical data directly into your agent's reasoning loop for immediate processing.

Analyze labor market shifts directly in your OpenAI Agents SDK production environment.

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Connect BLS JOLTS — Job Openings, Quits & Turnover MCP to OpenAI Agents SDK

Create your Vinkius account to connect BLS JOLTS — Job Openings, Quits & Turnover to OpenAI Agents SDK 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 BLS JOLTS — Job Openings, Quits & Turnover with OpenAI Agents SDK

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Claude

Perplexity

Pull JOLTS metrics into your agent

Call `get_jolts_data` to fetch current national labor turnover numbers. Your agent parses these figures to track the Great Resignation and labor market tightness without leaving your Python environment. This MCP Server provides the raw data your agent needs to build economic models. It feeds directly into your safety-guarded pipelines.

Run custom BLS series queries

Execute `query_bls` to grab specific timeseries data by using precise numerical codes. You define the lookup parameters and the agent handles the request sequence. This tool supports up to 50 concurrent lookbacks for heavy data analysis tasks. Your agents ingest these stats to inform decision-making logic.

Automate labor data tracking

Integrate this MCP Server into your agent flow to monitor monthly hires and fires automatically. Your agents stay updated on economic trends through these structured calls. Performance matters, so remember to set cacheToolsList to true in your configuration. This keeps your agent responsive during high-volume data requests.

Setup guide

Set up BLS JOLTS — Job Openings, Quits & Turnover MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all BLS JOLTS — Job Openings, Quits & Turnover tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives BLS JOLTS — Job Openings, Quits & Turnover tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate BLS JOLTS — Job Openings, Quits & Turnover tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="BLS JOLTS — Job Openings, Quits & Turnover Agent",
            instructions="You have access to BLS JOLTS — Job Openings, Quits & Turnover tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about BLS JOLTS — Job Openings, Quits & Turnover MCP in OpenAI Agents SDK

You instantiate the server via MCPServerStreamableHttp and pass it to your agent constructor. The SDK discovers your tools automatically, allowing you to call the data functions within your async context manager.

Yes, you use the query_bls tool with your specific series IDs. It returns the raw numerical data directly into your agent's reasoning loop for immediate processing.

Every tool call is fully visible within the OpenAI dashboard. You track the exact inputs and outputs of your labor market queries to ensure your agent logic remains sound.

The server allows 50 concurrent lookbacks per session. This prevents bottlenecking your agent when running complex multi-series analysis.

This server only reads public labor statistics from government databases. It never touches your private company records or sensitive credentials during its execution.

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