How do I get an OpenAQ API Key?

Visit [**OpenAQ Platform**](https://platform.openaq.org/), create a free account, and generate your API key from the dashboard. Free tier includes 1,000 requests/hour.

What air quality parameters can I monitor?

PM2.5, PM10, O3 (ozone), NO2, SO2, CO, BC (black carbon), and more. Use `get_parameters` to see the full list with units and classifications.

Can I get historical air quality data?

Yes! Use `get_measurements` with date_from and date_to parameters in ISO 8601 format to retrieve time-series data for any location and parameter.

OpenAQ MCP Server for Pydantic AI 9 tools — connect in under 2 minutes

Name: OpenAQ
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 9 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect OpenAQ through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to OpenAQ "
            "(9 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in OpenAQ?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 OpenAQ MCP Server

Connect OpenAQ, the world's largest open air quality database, to any AI agent and monitor real-time pollution levels, track air quality trends, and access data from thousands of monitoring stations globally through natural language.

Pydantic AI validates every OpenAQ tool response against typed schemas, catching data inconsistencies at build time. Connect 9 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Live Monitoring — Get the latest PM2.5, O3, NO2, SO2, and CO readings from any location worldwide
Historical Analysis — Query time-series measurement data with date range filters for trend analysis
Location Discovery — Browse monitoring stations by country, city, or geographic area
Sensor Tracking — View active sensor devices and their measurement parameters
Parameter Reference — Look up all measurable air quality parameters with units and classifications
Global Coverage — Access data from 100+ countries with thousands of active monitoring locations

The OpenAQ MCP Server exposes 9 tools through the Vinkius. Connect it to Pydantic AI 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 OpenAQ to Pydantic AI via MCP

Follow these steps to integrate the OpenAQ MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 9 tools from OpenAQ with type-safe schemas

Why Use Pydantic AI with the OpenAQ MCP Server

Pydantic AI provides unique advantages when paired with OpenAQ through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your OpenAQ integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your OpenAQ connection logic from agent behavior for testable, maintainable code

OpenAQ + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the OpenAQ MCP Server delivers measurable value.

Type-safe data pipelines: query OpenAQ with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple OpenAQ tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query OpenAQ and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock OpenAQ responses and write comprehensive agent tests

OpenAQ MCP Tools for Pydantic AI (9)

These 9 tools become available when you connect OpenAQ to Pydantic AI via MCP:

get_countries

Includes location counts and city counts per country. List countries with monitoring stations

get_latest_measurements

Useful for getting current air quality status without querying full history. Get latest measurements per location

get_location_by_id

Get details for a specific location

get_locations

Filter by country, city, parameter, or geographic bounding box. Returns location details including coordinates, sensor counts, and whether the station is an official monitor. List air quality monitoring locations

get_measurements

Filter by location, parameter, date range, and value range. Returns readings with timestamps. Get historical air quality measurements

get_parameter_by_id

Get details for a specific parameter

get_parameters

5, PM10, O3 (ozone), NO2, SO2, CO, etc. Includes units and whether each is a core parameter. List measurable air quality parameters

get_sensor_by_id

Get details for a specific sensor

get_sensors

Filter by location, parameter type, or active status. List air quality sensors

Example Prompts for OpenAQ in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with OpenAQ immediately.

"What's the current PM2.5 level in São Paulo, Brazil?"

"Which countries have the most air quality monitoring stations?"

"Show me ozone (O3) measurements from the last 24 hours in Paris."

Troubleshooting OpenAQ MCP Server with Pydantic AI

Common issues when connecting OpenAQ to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

OpenAQ + Pydantic AI FAQ

Common questions about integrating OpenAQ MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your OpenAQ MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.