How to Use the Google Air Quality MCP in AutoGen

Enable debate between AutoGen agents using real-time Google Air Quality data to reach consensus on environmental health decisions.

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Connect Google Air Quality MCP to AutoGen

Create your Vinkius account to connect Google Air Quality to AutoGen 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 AutoGen

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Perplexity

Debate air quality in AutoGen

Deploy `get_current_air_quality` to provide agents with the facts needed for their deliberations. One agent can act as the data fetcher while another evaluates the health impact. They discuss the findings until they reach a consensus. This prevents impulsive decisions based on incomplete environmental information.

Analyze trends with AutoGen agents

Task your agents with using `get_air_quality_history` to compare current conditions against past data. They challenge each other's interpretations of the trends. This process ensures the final health recommendation is vetted by multiple perspectives. The agents arrive at a conclusion only after weighing the historical context.

Consensus-driven health alerts

Use this MCP Server to feed data into a group chat of agents. A security agent flags high pollution, while a wellness agent suggests mitigation steps. They negotiate the best course of action based on the live numbers. The user receives a single, verified decision reached through internal debate.

Setup guide

Set up Google Air Quality MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Google Air Quality tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Google Air Quality_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Google Air Quality data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Google Air Quality_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Google Air Quality data")
    print(result.messages[-1].content)

Get your connection token →

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 AutoGen

Agents pass the tool outputs within the conversation thread. This allows every participant to see the raw metrics and challenge the conclusions drawn by their peers.

Yes. The agents can request historical data via the tool to support their arguments. They use this context to resolve disagreements about local environmental trends.

They continue the conversation until they resolve the discrepancy. The tools provide the ground truth, forcing the agents to align their final recommendation with the actual API readings.

The server uses a localized transport. Your data interaction remains within your agent execution environment, ensuring your specific location lookups are not exposed.

Agents handle AQI integers, pollutant strings, and health advice text. These are treated as distinct evidence points during the negotiation process.

Use it with your favorite AI tools

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