How to Use the Google Air Quality MCP in Google ADK

Q: How do I connect Google Air Quality to a Google ADK agent?

Use the McpToolset class in your Python code, passing the Vinkius HTTP URL to the server parameters so your Google ADK agent can discover the MCP Server tools. The agent immediately discovers getcurrentairquality and getairqualityhistory for use in Gemini workflows.

Q: Can I use Google Air Quality with BigQuery inside Google ADK?

Yes, write a Google ADK agent that fetches data using getcurrentairquality and writes the output directly to BigQuery. The Google ADK handles the data routing between the server tools and your cloud databases.

Feed raw Google Air Quality metrics and historical pollution data directly into your Google ADK pipelines for enterprise-scale analysis.

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Connect Google Air Quality MCP to Google ADK

Create your Vinkius account to connect Google Air Quality 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 Google Air Quality with Google ADK

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Pull live Google Air Quality metrics into Google ADK

The `get_current_air_quality` tool connects your Google ADK agent directly to live global air metrics to trigger automated alerts based on ozone and particulate levels. Your enterprise agents use these real-time values to update BigQuery tables or feed live dashboards inside your cloud environment. Because the Google ADK native tools support long-context Gemini models, your agent analyzes these current Google Air Quality metrics alongside massive local datasets. You get instant access to raw data without writing custom API integration code.

Query Google Air Quality history in Google ADK

This Google Air Quality MCP Server exposes the `get_air_quality_history` tool to help your Google ADK agent trace pollution trends over weeks or months. This allows your agent to run deep-dive analyses on urban air trends and output structured reports directly to your cloud storage. By using this server within the Google ADK framework, your agent cross-references historical Google Air Quality data with your existing enterprise datasets. The connection is handled over secure transports, keeping your architecture clean.

Pass Google Air Quality data to Google ADK agents

The `get_current_air_quality` tool provides structured JSON payloads that your Google ADK agent parses without hallucinating pollutant names or index values. This ensures that your long-context Gemini models receive accurate, raw numbers for complex reasoning tasks. Configure the Google ADK toolset to expose only this tool or pair it with history lookups depending on your project needs. The setup requires just a few lines of Python to get your agent querying live atmospheric data.

Setup guide

Set up Google Air Quality 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 Google Air Quality 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="Google Air Quality_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Google Air Quality tools via MCP.",
    tools=mcp_tools,
)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Google Air Quality. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Google Air Quality MCP in Google ADK

Use the `McpToolset` class in your Python code, passing the Vinkius HTTP URL to the server parameters so your Google ADK agent can discover the MCP Server tools. The agent immediately discovers `get_current_air_quality` and `get_air_quality_history` for use in Gemini workflows.

Yes, write a Google ADK agent that fetches data using `get_current_air_quality` and writes the output directly to BigQuery. The Google ADK handles the data routing between the server tools and your cloud databases.

Yes, this MCP Server supports both stdio and HTTP transports within the Google ADK environment. Run the server locally for development or host it on Vinkius for production Google Air Quality deployments.

The server returns compact, structured JSON payloads to keep Google ADK token usage low, even when querying historical data. This ensures your workflows remain fast and cost-effective.

Your latitude and longitude coordinates are processed in an ephemeral V8 sandbox to fetch current air metrics for Google ADK. No location history is recorded or stored on Vinkius servers, ensuring complete data privacy for your enterprise applications.

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