How to Use the BLS Public Data API MCP in Google ADK

Q: How do I configure the BLS Public Data API within a Google ADK agent?

Install the package with pip install google-adk and define your toolset using McpToolset. Pass that toolset object into your LlmAgent constructor to expose the tools to Gemini.

Feed BLS labor statistics directly to your Google ADK pipelines for deep, long-context reasoning with this MCP Server.

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Connect BLS Public Data API MCP to Google ADK

Create your Vinkius account to connect BLS Public Data API 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 Public Data API with Google ADK

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Large-Scale Economic Analysis with Google ADK

The `get_bls_timeseries_data` tool fetches decades of labor statistics directly into Gemini's massive context window. You can grab multiple series IDs at once and feed them right into your model for long-term trend analysis. This integration allows your Google ADK agents to process huge chunks of historical data without running out of memory. Gemini can digest thousands of data points and find correlations that smaller models miss.

Verifying Endpoint Availability

The `check_api_status` tool gives your enterprise pipelines a quick way to confirm the federal servers are responding. This keeps your Google Cloud workflows running smoothly without stalling on dead connections. By checking the server state first, your agent can decide whether to proceed with deep data crunching or queue the task for later. It keeps your automated cloud runs highly efficient.

Connecting MCP Server Tools to Cloud Databases

This MCP Server links federal data directly to your BigQuery and Vertex AI workflows. Your agent can pull fresh numbers from the government and immediately write them to your cloud data warehouse. You configure this by passing the toolset directly to your `LlmAgent`. There is no need to write custom ETL pipelines just to get basic economic indicators into your database.

Setup guide

Set up BLS Public Data API 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 Public Data API 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 Public Data API_agent",
    model="gemini-2.0-flash",
    instruction="You have access to BLS Public Data API tools via MCP.",
    tools=mcp_tools,
)

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Common questions about BLS Public Data API MCP in Google ADK

Install the package with `pip install google-adk` and define your toolset using `McpToolset`. Pass that toolset object into your `LlmAgent` constructor to expose the tools to Gemini.

Yes, you can use the optional tool names filter in the ADK setup. This lets you limit the agent to just `get_bls_timeseries_data` if you want to prevent status checking.

Yes, the ADK supports both Stdio and HTTP transports. You can connect to your managed Vinkius endpoint using the streamable HTTP server parameters.

Gemini's million-token context window is perfect for this. It easily ingests the massive JSON arrays returned by `get_bls_timeseries_data` without needing aggressive truncation.

Yes, Vinkius executes every tool call on this MCP Server in an isolated V8 sandbox. Your specific series ID queries and the returned federal labor statistics are processed ephemerally and never stored.

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