Bring Air Quality
to Pydantic AI
Create your Vinkius account to connect PurpleAir to Pydantic AI and start using all 10 AI tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code. No hosting, no server setup — just connect and start using.
Compatible with every major AI agent and IDE
What is the 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.
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.
How it works
- Subscribe to this server
- Enter your PurpleAir API Read Key
- Start querying air quality data through Claude, Cursor, or any MCP-compatible client
Who is this for?
- Environmental Scientists — track pollution trends and validate air quality models.
- Public Health Officials — issue health advisories during poor air quality events.
- Wildfire Response Teams — monitor smoke impacts in real-time across affected communities.
- Real Estate Professionals — assess air quality for property evaluations.
Built-in capabilities (10)
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
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
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
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
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
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
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
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
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
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
Why Pydantic AI?
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.
- —
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 in Pydantic AI
Why run PurpleAir with Vinkius?
The PurpleAir connection runs on our fully managed, secure cloud infrastructure. We handle the hosting, maintenance, and security so you don't have to deal with servers or code. All 10 tools are ready to work instantly without any complex setup.
You stay in complete control of your data. Your AI only accesses the information you approve, keeping your sensitive passwords and private details completely safe. Plus, with automatic optimizations, your AI works faster and more efficiently.

* Every connection is hosted and maintained by Vinkius. We handle the security, updates, and infrastructure so you don't have to write code or manage servers. See our infrastructure
Over 4,000 integrations ready for AI agents
Explore a vast library of pre-built integrations, optimized and ready to deploy.
Connect securely in under 30 seconds
Generate tokens to authenticate and link external services in a single step.
Complete visibility into every agent action
Audit live requests, latency, success rates, and active security compliance policies.
Optimize spending and track token ROI
Analyze real-time token consumption and cost metrics detailed by connection.




Explore our live AI Agents Analytics dashboard to see it all working
This dashboard is included when you connect PurpleAir using Vinkius. You will never be left in the dark about what your AI agents are doing with your tools.
PurpleAir and 4,000+ other AI tools. No hosting, no code, ready to use.
Professionals who connect PurpleAir to Pydantic AI through Vinkius don't need to write code, manage servers, or worry about security. Everything is pre-configured, secure, and runs automatically in the background.
Raw MCP | Vinkius | |
|---|---|---|
| Ready-to-use MCPs | Find and configure each manually | 4,000+ MCPs ready to use |
| Connection Setup | Manual coding & server setup | 1-click instant connection |
| Server Hosting | You host it yourself (needs 24/7 uptime) | 100% hosted & managed by Vinkius |
| Security & Privacy | Stored in plaintext config files | Bank-grade encrypted vault |
| Activity Visibility | Blind execution (no logs or tracking) | Live dashboard with real-time logs |
| Cost Control | Runaway AI token spend risk | Automatic budget limits |
| Revoking Access | Must delete files or code to stop | 1-click disconnect button |
How Vinkius secures
PurpleAir for Pydantic AI
Every request between Pydantic AI and PurpleAir is protected by our secure gateway. We automatically keep your sensitive data private, prevent unauthorized access, and let you disconnect instantly at any time.
Frequently asked questions
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.
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.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
Explore More MCP Servers
View all →
AbstractAPI
10 toolsEquip your AI agent with AbstractAPI's data enrichment — validate emails, geolocate IPs, check phone numbers, and enrich company data.

Weblate
32 toolsAutomate localization workflows via Weblate — manage projects, components, languages, and users directly from any AI agent.

Leiga
8 toolsManage agile projects with AI-assisted sprint planning, task prioritization, and team workload balancing that adapts in real time.

Magnolia (Enterprise Headless CMS)
10 toolsManage enterprise content via Magnolia CMS — query JCR nodes, audit template schemas, and orchestrate headless delivery.
