How to Use the BLS Labor Force — National Unemployment & CPS MCP in Google ADK

Q: How do I configure the BLS Labor Force — National Unemployment & CPS MCP Server with Google ADK?

Install the google-adk package and set up the McpToolset with your Vinkius HTTP URL. Pass this toolset to your LlmAgent to instantly expose the labor market tools.

Q: Can I use BLS Labor Force — National Unemployment & CPS to feed BigQuery datasets via Google ADK?

Yes, you can write an agent that fetches data using querybls and writes the output directly to BigQuery. This makes it easy to combine public macroeconomics with your private corporate data.

Feed real-time US labor market data directly into Google ADK pipelines for long-context Gemini analysis.

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Connect BLS Labor Force — National Unemployment & CPS MCP to Google ADK

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Setup BLS Labor Force — National Unemployment & CPS with Google ADK

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Inject live labor data into Google ADK

Feed live employment numbers directly into your Gemini models. Using `get_unemployment_rate` allows your enterprise agent to pull the official national unemployment figure from the Current Population Survey. This MCP Server works with Google ADK to let models reason over massive contexts. You can combine these labor stats with your internal business metrics to forecast regional hiring trends.

Run bulk series lookups via query_bls

When standard unemployment numbers are not enough, use `query_bls` to extract up to 50 series simultaneously. Your agent can pass explicit series IDs to fetch historical demographic breakdowns or participation rates. The tool feeds these timeseries data points straight into your agent's context window. From there, you can pipe the raw numbers directly into BigQuery for deeper SQL analysis.

Restrict tool access for safety

Google ADK lets you filter which operations are exposed to your agent. You can limit the model to only read the national unemployment rate by filtering the exposed toolset. This setup ensures your enterprise agents only call `get_unemployment_rate` when necessary, preventing unwanted API consumption. It keeps your system predictable and highly secure.

Setup guide

Set up BLS Labor Force — National Unemployment & CPS 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 Labor Force — National Unemployment & CPS 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 Labor Force — National Unemployment & CPS_agent",
    model="gemini-2.0-flash",
    instruction="You have access to BLS Labor Force — National Unemployment & CPS tools via MCP.",
    tools=mcp_tools,
)

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Common questions about BLS Labor Force — National Unemployment & CPS MCP in Google ADK

Install the `google-adk` package and set up the `McpToolset` with your Vinkius HTTP URL. Pass this toolset to your `LlmAgent` to instantly expose the labor market tools.

Yes, you can write an agent that fetches data using `query_bls` and writes the output directly to BigQuery. This makes it easy to combine public macroeconomics with your private corporate data.

Gemini models can process up to 1 million tokens, meaning you can pull 50 concurrent series using `query_bls` without worrying about context limits. The agent can ingest decades of historical CPS data in a single turn.

Yes, Google ADK supports both Stdio and HTTP transports. For hosted Vinkius environments, you will use the Streamable HTTP server parameters to connect.

Your requests for national unemployment rates and timeseries data are routed through this MCP connection. No query parameters or series IDs are logged or stored on Vinkius servers. All processing occurs in temporary, isolated execution containers that self-destruct after returning the data.

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