AirVisual MCP. Get Real-Time AQI and Weather Data Globally
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
AirVisual monitors global air quality and weather conditions instantly. It gives your agent real-time data on AQI, pollution levels, and meteorology for any location worldwide.
You can check current readings for a specific city, find the nearest station using coordinates or IP address, or map out supported locations by country/state.
What your AI agents can do
Get city data
Retrieves real-time AQI and weather data for a specific city you name.
Get nearest city by coords
Gets the air quality and weather metrics for the closest city using GPS coordinates.
Get nearest city by ip
Finds and retrieves data for the nearest monitored city based on your IP address.
Retrieve current air quality index and weather metrics for any specified city.
Pull data for the closest monitoring site using precise GPS latitude/longitude inputs.
Determine and retrieve environmental metrics based on your current network IP address.
Access granular, detailed data from an individual monitoring station location.
Browse the hierarchy to list all supported countries, states, or cities for future queries.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
AirVisual with 7 Tools
These tools let your agent perform specific lookups: listing supported regions, finding the nearest city via IP or coordinates, or pulling granular data from individual monitoring stations.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using AirVisual on Vinkius019e5cf8get city data
Retrieves real-time AQI and weather data for a specific city you name.
019e5cf8get nearest city by coords
Gets the air quality and weather metrics for the closest city using GPS coordinates.
019e5cf8get nearest city by ip
Finds and retrieves data for the nearest monitored city based on your IP address.
019e5cf8get station data
Pulls granular environmental readings directly from a specific, named monitoring station.
019e5cf8list cities
Lists all supported cities within a specified state or region.
019e5cf8list countries
Returns a list of every country the service monitors for air quality data.
019e5cf8list states
Provides a list of supported states within a specific country.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with AirVisual, then connect any of our 4,800+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,800+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AirVisual. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS INFRASTRUCTURE
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EU data residency
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~60% cost reduction
Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 7 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Getting Global Environmental Data Used To Be a Pain.
Before this MCP, getting current air quality data meant opening multiple tabs: one for the national weather service, another for a local pollution index (AQI), and maybe a third to check historical records. You'd have to copy-paste city names or coordinates repeatedly across these different services just to build a single picture of environmental health.
Now you ask your agent once. It handles the geo-lookup—whether by IP or GPS—and pulls all current data points together. You get everything in one structured response, ready for your script or report.
Accessing Air Quality Data with `get_station_data`
The old way forced you to rely on city-level averages, which are useless if the pollution source is only one block away. You had to physically drive to a monitoring office or use an outdated map service just to find the ID for a granular data pull.
Now, `get_station_data` lets your agent read specific environmental metrics from individual stations. It's hyper-local detail that changes what you can do with the information.
What you can do with this MCP connector
Need to know what's really going on with the air? This MCP connects environmental monitoring data—AQI and weather—directly into your workflow. Instead of opening three different websites for pollution, climate forecasts, and local maps, you ask your agent once. It handles all that heavy lifting. You can drill down from a global overview to hyper-local readings from specific monitoring stations.
The Vinkius catalog makes this connection simple; just subscribe and grant access. Your agent uses these tools to pull current AQI for major cities or pinpoint the exact environmental data nearest your IP address, letting you plan outdoor activities or run research without leaving your client app.
019e5cf9-15d4-7027-82e8-99c3ab9b1a16 
How AirVisual MCP Works
- 1 Subscribe to this MCP and provide your AirVisual API key.
- 2 Your agent sends a request with specific location parameters (e.g., coordinates or city name).
- 3 The MCP executes the correct tool call, returning real-time AQI, weather, and pollution metrics directly to your client.
The bottom line is that you just ask for the data; the system figures out where it needs to look.
Who Is AirVisual MCP For?
Environmental researchers who need global, structured datasets. Health-conscious users who plan activities around pollution risk. Ops engineers integrating real-time environmental triggers into automated systems.
Gathers time-series AQI data from thousands of global stations to train predictive models on air quality degradation.
Determines the pollution risk profile for a client's proposed site by querying nearest city and station data based on GPS input.
Sets up automated workflows that trigger alerts or HVAC adjustments when local air quality falls below safe thresholds.
What Changes When You Connect
- You stop guessing about air quality. Instead of checking multiple websites, using
get_city_dataprovides immediate, single-source metrics for any city. - Need data right now? Use
get_nearest_city_by_ip. You just need to ask your agent what's nearby, and it uses your IP address to pull the air quality report. - Drill down past general readings. If you know a specific monitoring spot matters, use
get_station_datafor hyper-local insights. - Building a script? First, run
list_countries, thenlist_states, and finallylist_citiesto validate the full scope of supported locations before querying data. - The system automatically handles location resolution. If you give it coordinates, use
get_nearest_city_by_coordsinstead of trying to guess the city name.
Real-World Use Cases
Planning a Field Site Inspection
An environmental consultant needs to assess air quality risk for an industrial park. The agent first uses list_states and list_cities to confirm the region, then uses get_nearest_city_by_coords with the site's GPS markers to generate a full pollution report.
Emergency Response Team Dispatch
A dispatcher needs immediate local data. They simply ask 'What is the air quality here?' The agent uses get_nearest_city_by_ip and returns the current AQI, temperature, and wind speed instantly.
Developing a Global Health Alert System
A developer needs to build alerts for multiple regions. They use list_countries to iterate through all available markets, then call get_city_data sequentially to check AQI in major urban centers.
IoT Air Quality Triggering
A smart building manager wants the HVAC system to adjust when pollution hits a high mark. The agent monitors the specific station data using get_station_data and triggers the automation if AQI exceeds 100.
The Tradeoffs
Using only country names
Asking your agent, 'Give me pollution data for India.' This is too broad. The agent can't guess the city or state you mean.
→
You must narrow it down. First, use list_states to get the states in India. Then, provide a specific city name and run get_city_data. If you prefer coordinates, use get_nearest_city_by_coords.
Assuming IP data is enough
Relying only on get_nearest_city_by_ip when the site moves. The IP might resolve to a cell tower, not the actual location.
→
If you need high precision, always use GPS coordinates with get_nearest_city_by_coords. This bypasses network limitations and targets the physical spot.
Asking for all data at once
A single prompt asking to 'List countries, check nearest IP city, and get station data.' The agent will fail or only run one tool.
→
Break it into steps. First, use list_countries to verify the region scope. Then, execute a targeted query like get_nearest_city_by_ip.
When It Fits, When It Doesn't
Use this MCP if your primary need is real-time, location-specific environmental metrics (AQI and weather). Use it when you require the data to be pulled into an automated workflow or client application. Don't use this if you just want a general overview; checking Google Maps for 'weather in Tokyo' works fine then. You only need AirVisual if your process requires structured, programmatic access: check specific cities with get_city_data when you know the name; use get_nearest_city_by_coords or get_nearest_city_by_ip when location is dynamic or unknown; and use listing tools (list_countries, etc.) only to map out possible parameters before running a query.
Common Questions About AirVisual MCP
How does get_nearest_city_by_ip work? +
It determines the nearest monitored city using your current IP address. It's fast, but remember this only gives a general area reading; for precision, use get_nearest_city_by_coords.
Can I list all available locations with list_countries? +
Yes, running list_countries provides the full scope of nations supported. You can then follow up by using list_states to see the sub-regions within that country.
Is get_city_data better than getting station data? +
It depends on your need. Use get_city_data for a quick, general read of an entire urban area. Use get_station_data when you need the exact metrics from one physical monitoring equipment.
What if I don't know the state abbreviation? +
Run list_countries first to narrow down the nation, and then use list_states. This gives you a clear list of supported states for that country.
What happens if I use an incorrect API key when calling get_city_data? +
The call fails with a 401 Unauthorized error. Always confirm your AirVisual API Key is active and properly entered into the Vinkius setup. You can verify credentials through the dedicated support portal.
How frequently does get_station_data update its readings? +
The data freshness depends on the physical sensor at that station. While AirVisual provides real-time indexing, the actual reporting interval varies by location and pollutant type. Always check the reported timestamp for accuracy.
If I use get_nearest_city_by_coords with coordinates outside a monitored area, what error do I receive? +
You will likely receive a 'No Data Available' or similar geographic boundary error. Make sure your GPS coordinates fall within the service coverage map provided by AirVisual to guarantee results.
Are there rate limits when using list_cities repeatedly for many states? +
Yes, repeated bulk calls can trigger temporary rate limiting. Implement exponential backoff or use batch processing methods in your agent's code to avoid hitting API caps and ensure consistent performance.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.