What's the difference between European AQI and US AQI?

The **European AQI** ranges 0-100+ with thresholds set by the EEA. The **US AQI** ranges 0-500 with EPA thresholds. Both are provided simultaneously so your AI can serve users in any region with their local standard.

Open-Meteo Air Quality MCP Server for LlamaIndex 4 tools — connect in under 2 minutes

Name: Open-Meteo Air Quality
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 4 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Open-Meteo 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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python

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 Open-Meteo Air Quality. "
            "You have 4 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Open-Meteo Air Quality?"
    )
    print(response)

asyncio.run(main())

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About Open-Meteo Air Quality MCP Server

Give your AI the power to assess air safety with real-time pollutant data at 11km resolution.

LlamaIndex agents combine Open-Meteo Air Quality tool responses with indexed documents for comprehensive, grounded answers. Connect 4 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

Pollutant Concentrations — PM2.5, PM10, O₃, NO₂, SO₂, CO, dust, and ammonia in μg/m³
AQI Indexes — Both European (0-100+) and US (0-500) Air Quality Indexes with per-pollutant breakdowns
Pollen Forecast — Birch, grass, alder, ragweed, olive, and mugwort pollen counts for allergy planning
UV Index — Clear-sky and actual UV index for sun exposure safety

The Open-Meteo Air Quality MCP Server exposes 4 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 Open-Meteo Air Quality to LlamaIndex via MCP

Follow these steps to integrate the Open-Meteo 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 4 tools from Open-Meteo Air Quality

Why Use LlamaIndex with the Open-Meteo Air Quality MCP Server

LlamaIndex provides unique advantages when paired with Open-Meteo Air Quality through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Open-Meteo Air Quality tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Open-Meteo Air Quality tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Open-Meteo Air Quality, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Open-Meteo Air Quality tools were called, what data was returned, and how it influenced the final answer

Open-Meteo Air Quality + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Open-Meteo Air Quality MCP Server delivers measurable value.

Hybrid search: combine Open-Meteo Air Quality real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Open-Meteo 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 Open-Meteo Air Quality for fresh data

Analytical workflows: chain Open-Meteo Air Quality queries with LlamaIndex's data connectors to build multi-source analytical reports

Open-Meteo Air Quality MCP Tools for LlamaIndex (4)

These 4 tools become available when you connect Open-Meteo Air Quality to LlamaIndex via MCP:

get_air_quality

5, PM10, ozone, nitrogen dioxide, sulphur dioxide, and carbon monoxide concentrations for any location. Get air quality pollutant concentrations

get_aqi_index

Get Air Quality Index (European and US standards)

get_pollen_forecast

Get pollen and allergen forecast

get_uv_index

Get UV index forecast

Example Prompts for Open-Meteo Air Quality in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Open-Meteo Air Quality immediately.

"Is the air quality in Beijing safe for outdoor exercise today?"

"What's the pollen forecast for Berlin this week?"

"What's the UV index in Sydney right now?"

Troubleshooting Open-Meteo Air Quality MCP Server with LlamaIndex

Common issues when connecting Open-Meteo Air Quality to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Open-Meteo Air Quality + LlamaIndex FAQ

Common questions about integrating Open-Meteo Air Quality MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Open-Meteo Air Quality tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.