How do I find air quality near my address?

Use the `get_sensors_near_me` tool with your latitude and longitude. Alternatively, search for sensors by bounding box using `get_sensors_by_bounding_box` with corner coordinates. The API will return nearby sensors with current PM2.5, temperature, and humidity readings.

Can I get historical air quality data?

Yes. Use `get_sensor_history` with a sensor index and time range (Unix timestamps). You can specify which fields to retrieve (PM2.5, temperature, humidity) and set an averaging interval (e.g. 3600 for hourly averages). For spreadsheet analysis, use `get_sensor_history_csv` to get data in CSV format.

What does PM2.5 mean and why is it important?

PM2.5 refers to fine particulate matter smaller than 2.5 micrometers — about 30 times smaller than a human hair. These particles can penetrate deep into lungs and enter the bloodstream, causing respiratory and cardiovascular health effects. The WHO guideline is 5 µg/m³ annual average. PurpleAir sensors measure PM2.5 in real-time, making them essential for health advisories and pollution monitoring.

PurpleAir MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: PurpleAir
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect PurpleAir through 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 PurpleAir "
            "(10 tools)."
        ),
    )

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

asyncio.run(main())

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* 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 PurpleAir MCP Server

Access the world's largest hyperlocal air quality dataset through PurpleAir — a global network of over 50,000 low-cost air quality sensors measuring PM2.5, PM10.0, temperature, humidity, pressure, and more. Connect PurpleAir to your AI agent to monitor real-time air quality, track wildfire smoke, analyze pollution trends, and access historical data for any location — all through natural conversation.

Pydantic AI validates every PurpleAir tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through 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

Real-Time Air Quality — Get current PM2.5 readings from sensors near any address or coordinate.
Historical Analysis — Retrieve time-series data for trend analysis, pollution events, and compliance reporting.
Geographic Mapping — Find all sensors within a bounding box for city-wide or regional air quality mapping.
Wildfire Smoke Tracking — Monitor PM2.5 spikes during wildfire events across affected areas.
Indoor Air Quality — Access indoor sensor data for workplace health and HVAC optimization.
CSV Export — Download historical data in CSV format for spreadsheet analysis.
Location-Based Queries — Find the closest sensor to any GPS coordinate.
Sensor Filtering — Filter sensors by type (indoor/outdoor), fields, and update recency.

The PurpleAir MCP Server exposes 10 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 PurpleAir to Pydantic AI via MCP

Follow these steps to integrate the PurpleAir 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 10 tools from PurpleAir with type-safe schemas

Why Use Pydantic AI with the PurpleAir MCP Server

Pydantic AI provides unique advantages when paired with PurpleAir 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 PurpleAir integration code

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

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

PurpleAir + Pydantic AI Use Cases

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

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

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

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

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

PurpleAir MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect PurpleAir to Pydantic AI via MCP:

get_indoor_sensors

These sensors measure air quality inside buildings, homes, and enclosed spaces. Useful for indoor air quality assessments, HVAC monitoring, and workspace health studies. Get all indoor PurpleAir sensors

get_outdoor_sensors

These are sensors measuring ambient outdoor air quality. Returns current PM2.5, temperature, humidity and other measurements for each sensor. Useful for regional air quality monitoring, wildfire smoke tracking, and urban pollution studies. Get all outdoor (outside) PurpleAir sensors

get_pm25_sensors

5 (fine particulate matter) measurements. PM2.5 is the most important air quality indicator — particles smaller than 2.5 micrometers that can penetrate deep into lungs and bloodstream. Returns current PM2.5 concentrations along with location data. Essential for health advisories, wildfire smoke tracking, and urban pollution monitoring. Get sensors with PM2.5 measurements

get_sensor_data

Returns PM2.5, PM1.0, PM10.0 particle concentrations, temperature, humidity, pressure, VOC levels, and other measurements depending on the sensor model. Use the fields parameter to specify which measurements to return. Essential for monitoring air quality at a specific location. Get real-time data from a specific PurpleAir sensor

get_sensor_history

Returns time-series data for the requested fields (PM2.5, temperature, humidity, etc.) at regular intervals. Use start_timestamp and end_timestamp (Unix timestamps) to define the time range. The average parameter controls data aggregation (e.g. 60 for 1-minute averages, 3600 for hourly). Essential for analyzing air quality trends, identifying pollution events, and compliance reporting. Get historical air quality data from a PurpleAir sensor

get_sensor_history_csv

Same functionality as get_sensor_history but returns data as CSV instead of JSON. Use for offline analysis, charting, or compliance reporting. Requires start_timestamp and end_timestamp parameters. Get historical sensor data in CSV format for analysis

get_sensors_by_bounding_box

Provide the northwest (nwlat, nwlng) and southeast (selat, selng) corner coordinates. Perfect for mapping air quality across a city, neighborhood, or region. Returns all sensors in the area with current readings. Use with fields parameter to customize returned data. Get all sensors within a geographic bounding box

get_sensors_by_index

Provide comma-separated sensor indices in the show_only parameter. Useful when you already know the sensor indices from a previous query and want to get fresh readings without fetching all sensors. Get data for specific sensor(s) by their indices

get_sensors_near_me

Internally uses a bounding box around the point to find nearby sensors. Useful for identifying the closest PurpleAir monitor to any address or coordinate. Returns sensors sorted by proximity with current air quality readings. Find PurpleAir sensors near a specific location

list_sensors

Use the location_type parameter to filter by sensor type (outside=0, inside=1). Use the fields parameter to specify which data fields to return (e.g. name,latitude,longitude,pm2.5_atm,temperature,humidity). By default returns basic sensor info. Use show_only to filter by specific sensor indices (comma-separated). Use modified_since (Unix timestamp) to get only sensors updated after a specific time. Results include sensor metadata and real-time air quality measurements. List PurpleAir air quality sensors with optional filters

Example Prompts for PurpleAir in Pydantic AI

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

"What's the air quality near San Francisco right now?"

"Show me the PM2.5 trend for sensor 12345 over the last 24 hours."

"Find all outdoor sensors in Los Angeles and show me their PM2.5 readings."

Troubleshooting PurpleAir MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

PurpleAir + Pydantic AI FAQ

Common questions about integrating PurpleAir 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 PurpleAir MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.