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

Q: Can I filter tools when using BLS JOLTS — Job Openings, Quits & Turnover with Google ADK?

You absolutely can. Use the toolnames filter during initialization to ensure your agent only accesses the specific labor metrics you need for your current task.

Connect federal labor data to your Google ADK pipelines on Google Cloud.

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Connect BLS JOLTS — Job Openings, Quits & Turnover MCP to Google ADK

Create your Vinkius account to connect BLS JOLTS — Job Openings, Quits & Turnover to Google ADK 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 Google ADK

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Access JOLTS data in Google ADK

Utilize `get_jolts_data` to import national labor turnover stats directly into your Gemini-powered agent. Your agents can reason over these figures to interpret labor market trends. This setup allows you to combine public economic data with your internal BigQuery datasets. Your agent acts on the combined context to provide deeper analysis.

Execute series-specific BLS lookups

Invoke `query_bls` to target exact series IDs and pull specific labor metrics into your environment. Your agents handle these requests as part of a broader Google Cloud workflow. Use the tool_names filter to restrict which functions your agent can see. This keeps your agent focused on the relevant labor data.

Scale data analysis on Google Cloud

Run this MCP Server to feed labor market shifts into your long-context Gemini models. Your agents process large datasets while maintaining state across the entire reasoning chain. Since this supports Stdio and HTTP transports, you have flexibility in your deployment architecture. It fits right into your existing Google infrastructure.

Setup guide

Set up BLS JOLTS — Job Openings, Quits & Turnover MCP in Google ADK

Prerequisites

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

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with BLS JOLTS — Job Openings, Quits & Turnover tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="BLS JOLTS — Job Openings, Quits & Turnover_agent",
    model="gemini-2.0-flash",
    instruction="You have access to BLS JOLTS — Job Openings, Quits & Turnover tools via MCP.",
    tools=mcp_tools,
)

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

You initialize the McpToolset with your server parameters and pass it to your LlmAgent. The framework then exposes the labor data tools to your agent's reasoning capabilities.

Yes, Gemini models handle the 1M+ token context window, making it easy to reason over massive amounts of labor data. You get the insights without manual data cleaning.

You absolutely can. Use the tool_names filter during initialization to ensure your agent only accesses the specific labor metrics you need for your current task.

Your agents compare public labor statistics against your private corporate metrics stored in BigQuery. This gives you a clear view of how your turnover compares to the national average.

The server transmits only public government labor metrics. It operates as a read-only bridge to the BLS, ensuring no internal information leaves your secure Google Cloud environment.

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