Google Air Quality MCP Server for LlamaIndex 2 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Google Air Quality as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Google Air Quality. "
"You have 2 tools available."
),
)
response = await agent.run(
"What tools are available in Google Air Quality?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Google Air Quality MCP Server
Equip your AI agent with hyper-local environmental intelligence through the Google Air Quality MCP server. This integration provides real-time access to accurate air quality indexes, detailed pollutant concentrations, and actionable health recommendations for specific coordinates. Powered by Google's massive environmental data layer, your agent can retrieve the Universal Air Quality Index (UAQI), identify dominant pollutants (PM2.5, NO2, etc.), and access up to 30 days of historical data. Whether you are building health-tracking tools, planning outdoor events, or researching urban pollution, your agent acts as a dedicated environmental consultant through natural conversation.
LlamaIndex agents combine Google Air Quality tool responses with indexed documents for comprehensive, grounded answers. Connect 2 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Real-time AQI Lookup — Get the current Universal Air Quality Index for any latitude/longitude.
- Pollutant Breakdown — Identify dominant pollutants and their concentrations in specific areas.
- Historical Auditing — Retrieve up to 720 hours of historical air quality data for trend analysis.
- Health Advice — Access tailored recommendations for children, elderly, and sensitive groups.
The Google Air Quality MCP Server exposes 2 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Google Air Quality to LlamaIndex via MCP
Follow these steps to integrate the Google Air Quality MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 2 tools from Google Air Quality
Why Use LlamaIndex with the Google Air Quality MCP Server
LlamaIndex provides unique advantages when paired with Google Air Quality through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Google Air Quality tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Google Air Quality tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Google Air Quality, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Google Air Quality tools were called, what data was returned, and how it influenced the final answer
Google Air Quality + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Google Air Quality MCP Server delivers measurable value.
Hybrid search: combine Google Air Quality real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Google Air Quality to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Google Air Quality for fresh data
Analytical workflows: chain Google Air Quality queries with LlamaIndex's data connectors to build multi-source analytical reports
Google Air Quality MCP Tools for LlamaIndex (2)
These 2 tools become available when you connect Google Air Quality to LlamaIndex via MCP:
get_air_quality_history
Get historical air quality data
get_current_air_quality
Get current air quality using Google Maps API
Example Prompts for Google Air Quality in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Google Air Quality immediately.
"What is the air quality in San Francisco right now?"
"Show me the air quality history for Tokyo for the last 24 hours."
"Are there any health warnings for Beijing today?"
Troubleshooting Google Air Quality MCP Server with LlamaIndex
Common issues when connecting Google Air Quality to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGoogle Air Quality + LlamaIndex FAQ
Common questions about integrating Google Air Quality MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Google Air Quality with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Google Air Quality to LlamaIndex
Get your token, paste the configuration, and start using 2 tools in under 2 minutes. No API key management needed.