How to Use the BLS Public Data API MCP in OpenAI Agents SDK

Q: How do I install the BLS Public Data API tool inside my OpenAI Agents SDK project?

Run pip install openai-agents to get started. Instantiate the connection with MCPServerStreamableHttp using your Vinkius endpoint, then pass the server object into your Agent constructor.

Q: What happens if the BLS endpoint is down during a run?

Your agent should call checkapistatus first to verify the endpoint is active. If the service is down, your agent can catch the error and gracefully pause execution.

Feed real-time US labor statistics directly to your OpenAI Agents SDK workflows with this managed MCP connection.

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Connect BLS Public Data API MCP to OpenAI Agents SDK

Create your Vinkius account to connect BLS Public Data API 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 Public Data API with OpenAI Agents SDK

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Direct BLS Series Querying with OpenAI Agents SDK

The `get_bls_timeseries_data` tool pulls raw economic numbers directly into your agent's context window. You pass the exact series IDs you need, and the tool returns structured historical data without making you write custom scraper scripts. This setup lets your agents analyze employment trends or inflation metrics on the fly. Because OpenAI Agents SDK handles agent handoffs, you can route this data to a specialist agent for deep financial analysis.

Real-Time Server Health Monitoring

The `check_api_status` tool lets your agent verify that the Bureau of Labor Statistics endpoint is up before running heavy analytical tasks. This prevents your production runs from hanging or throwing unhandled exceptions mid-execution. Your agent checks the status programmatically, saving API credits by avoiding failed runs. If the server is down, the agent halts or alerts your team instead of spinning its wheels.

Production-Grade Guardrails and Tracing

This MCP Server integrates the `get_bls_timeseries_data` tool with OpenAI's tracing dashboard to log every single economic query your agent makes. Every tool call gets logged, so you can trace exactly how your agent interprets inflation trends. You can set strict rules before the agent executes the query tool. This stops the model from requesting random, non-existent series IDs and wasting your rate limits.

Setup guide

Set up BLS Public Data API 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 Public Data API tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives BLS Public Data API 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 Public Data API 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 Public Data API Agent",
            instructions="You have access to BLS Public Data API 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 Public Data API MCP in OpenAI Agents SDK

Run `pip install openai-agents` to get started. Instantiate the connection with `MCPServerStreamableHttp` using your Vinkius endpoint, then pass the server object into your Agent constructor.

Yes, the SDK automatically discovers the tools when you register the server. Set the `cacheToolsList` parameter to true to speed up boot times for your agent instances.

The SDK relies on your agent's system prompts and internal loop constraints to manage calls. You can configure execution guards to prevent your agent from repeatedly calling `get_bls_timeseries_data` in a tight loop.

Your agent should call `check_api_status` first to verify the endpoint is active. If the service is down, your agent can catch the error and gracefully pause execution.

Vinkius runs this MCP Server inside an isolated, zero-trust sandbox. Your queries for economic series IDs and the returned labor statistics never touch third-party storage, keeping your research private.

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