How to Use the Google Air Quality MCP in LlamaIndex

Q: Can I search past Google Air Quality reports in LlamaIndex?

Absolutely. By calling getairqualityhistory and indexing the response, you make historical trends a permanent, searchable part of your knowledge base.

Turn LlamaIndex into a searchable environmental database by indexing Google Air Quality API results for RAG applications.

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Connect Google Air Quality MCP to LlamaIndex

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

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Index air quality data in LlamaIndex

Use `get_current_air_quality` to pull live readings and push them into your vector store. Your index now contains current environmental state alongside your existing documents. This allows your RAG application to answer queries about current conditions with grounded, real-time facts. The agent avoids hallucinations by referencing the indexed API output.

Query historical air quality in LlamaIndex

Run `get_air_quality_history` to populate your knowledge base with past environmental trends. You can perform semantic searches across these historical snapshots to find patterns. Your application treats this data like any other document in the index. It's fully queryable through your established LlamaIndex workflows.

Ground AI answers with live API data

Combine the `get_current_air_quality` tool with your existing data loaders to create a unified knowledge source. The agent references specific pollutant levels to provide evidence-backed responses. This setup ensures your agent isn't guessing about the environment. It relies on the current data fetched directly from the Google API.

Setup guide

Set up Google Air Quality MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Google Air Quality MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Google Air Quality tools.",
)
response = await agent.run("List recent Google Air Quality data")

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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 LlamaIndex

The tool output is converted into a format your index can ingest. Once indexed, these readings become searchable knowledge chunks within your vector store.

Absolutely. By calling `get_air_quality_history` and indexing the response, you make historical trends a permanent, searchable part of your knowledge base.

It provides precise, source-verified metrics. By indexing these, you ensure your RAG application provides answers grounded in factual, time-stamped information.

The server operates via a controlled endpoint. Your data is handled in memory, and sensitive environment keys never leave your local environment during the indexing process.

The server returns structured JSON containing AQI, pollutant types, and health advice. This structure is preserved so your index can filter by specific environmental factors.

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