AirVisual MCP. Track pollution and weather data by location.
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AirVisual monitors global air quality and weather data. Connect it to your AI agent to get real-time AQI, pollution levels (like PM2.5), and current weather conditions for any city or station worldwide.
You can check the nearest location by IP, list supported countries, or pull granular data from specific monitoring stations.
What your AI agents can do
Get city data
Retrieves the current AQI and weather data for a specific city name.
Get nearest city by coords
Calculates and returns the air quality data for the city nearest the provided GPS coordinates.
Get nearest city by ip
Uses the request IP address to find and retrieve data for the closest city.
Retrieves current AQI and weather readings for a named city.
Uses your IP address to locate the nearest city and pull its current air quality data.
Calculates the nearest city based on provided latitude and longitude coordinates.
Pulls highly detailed environmental data from an individual station ID.
Provides lists of supported countries, states, and cities for scoping searches.
Accesses the latest combination of air quality index and meteorological readings.
Ask AI about this MCP
Supported MCP Clients
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AirVisual MCP Server: 7 Tools for Environmental Data
Access real-time air quality indices, pollutant levels, and weather data using seven specialized tools.
019e5cf8get city data
Retrieves the current AQI and weather data for a specific city name.
019e5cf8get nearest city by coords
Calculates and returns the air quality data for the city nearest the provided GPS coordinates.
019e5cf8get nearest city by ip
Uses the request IP address to find and retrieve data for the closest city.
019e5cf8get station data
Pulls granular, detailed environmental data from a specific monitoring station ID.
019e5cf8list cities
Lists all supported cities within a specified country and state.
019e5cf8list countries
Provides a list of all countries supported by the AirVisual API.
019e5cf8list states
Lists all supported states or provinces within a given 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 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ 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
What you can do with this MCP connector
Your AI agent uses the AirVisual MCP Server to pull real-time air quality and weather data globally. You can check the current AQI and weather readings for any named city using get_city_data. You don't need to know the exact location; your agent can find the nearest city and pull data just from your IP address using get_nearest_city_by_ip.
If you've got GPS coordinates, you can calculate the nearest city and pull its air quality data using get_nearest_city_by_coords. For deep environmental checks, you can pull highly detailed data from an individual monitoring station using get_station_data. To scope your search, your agent can list every supported country with list_countries, and then list all states or provinces within a given country using list_states, or list supported cities within a specific country and state using list_cities.
How AirVisual MCP Works
- 1 Subscribe to the AirVisual server and provide your API Key.
- 2 Your AI agent executes a tool call (e.g.,
get_city_data) and passes the required parameters (city name, coordinates, etc.). - 3 The server runs the query against the AirVisual API and sends back structured JSON containing the AQI, pollutant breakdown, and weather metrics.
The bottom line is that your agent handles the API calls, and you get structured, real-world environmental data back.
Who Is AirVisual MCP For?
The environmental researcher who needs to gather real-time data from thousands of global stations for comparison. The healthcare professional needing to track local pollution levels near a patient's home. The smart home automation developer integrating air quality into daily routines.
Gathers real-time data from multiple global stations to track pollution trends and perform large-scale geographical analysis.
Determines optimal outdoor activity recommendations or adjusts patient care plans based on local air quality and allergen data.
Writes workflows that trigger actions (like sending alerts or adjusting HVAC) when local air quality drops below a set threshold.
What Changes When You Connect
- See the full scope of environmental data. You can list all supported countries via
list_countriesand then drill down throughlist_statesandlist_citiesto narrow your search area. - Pinpoint the exact location without manual input. Use
get_nearest_city_by_ipto instantly pull air quality data based on where the user is sending the query. - Access hyper-local readings. If a city is too broad, use
get_station_datawith a specific monitoring station ID to get granular pollutant details. - Automate location discovery. If you only know the general area, use
get_nearest_city_by_coordsto find the closest available data point using GPS coordinates. - Maintain a clear data flow. The combination of
list_countriesandlist_stateslets your agent validate the search scope before runningget_city_data, preventing invalid calls. - Handle multiple input types. You don't need to guess the user's input type; you can check by IP, by coordinates, or by city name using dedicated tools.
Real-World Use Cases
Assessing a remote site's air quality
A field agent needs to check the air quality at a temporary job site. They ask their agent, 'What's the air quality near these coordinates?' The agent runs get_nearest_city_by_coords, finds the closest city, and pulls the current AQI and weather report for the site.
Quick check on current pollution levels
A user asks their agent, 'What's the air quality here?' The agent immediately runs get_nearest_city_by_ip to use the user's IP address, solving the problem without requiring manual location input.
Building a global data reference list
A developer needs to build a dropdown menu of supported locations. The agent first runs list_countries, then iterates through list_states for a given country, and finally uses list_cities to populate the full scope, ensuring no location is missed.
Deep dive on a specific pollution source
A researcher suspects a specific factory is causing pollution. Instead of relying on general city data, they use get_station_data with the precise station ID to pull highly granular data on specific pollutants.
The Tradeoffs
Calling `get_city_data` without validation
A developer just passes a random city name like 'Nowhereville' to get_city_data and expects data, wasting an API call and getting a generic error.
→
First, use list_countries to confirm the country code, then list_states to find the correct state, and finally list_cities to validate the name before calling get_city_data. This ensures the location is supported.
Ignoring IP location data
A user asks for air quality but the developer only builds logic around explicit city names, failing when the user simply says, 'What's the air quality right now?'
→
Always check get_nearest_city_by_ip first. This is the most natural way to query the tool and immediately provides a relevant location and its data.
Over-relying on coordinates
A user gives vague coordinates and the developer only uses get_nearest_city_by_coords, missing the chance to get specific station data.
→
After using get_nearest_city_by_coords to get the city, check if the API response includes a nearby station ID. If it does, use get_station_data for the most precise reading.
When It Fits, When It Doesn't
Use this server if your application needs to answer questions about current environmental conditions (AQI, PM2.5, weather) across different geographical scopes. You need to support queries based on city names, IP addresses, or GPS coordinates. Don't use this if you only need historical data—AirVisual handles real-time metrics. If your primary need is just to validate a list of locations, use list_countries, list_states, and list_cities in isolation. If you need the absolute most granular data, you must use get_station_data and provide a station ID. If you need a general overview, stick to get_city_data.
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.
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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.
Available Capabilities
Checking pollution levels shouldn't require a database join.
Today, checking air quality often means jumping between a city search page, a state selector, and then a specific monitoring station page. You copy the city name, switch tabs to the coordinates tool, paste the latitude, and then click 'Search.' This process takes minutes and requires three separate actions just to get one data point.
With AirVisual, your agent handles the sequence. You ask, 'What's the air quality in London?' Your agent uses `get_city_data` and returns the current AQI, weather, and pollutant breakdown instantly. No tabs, no copy-pasting. Just the data.
AirVisual MCP Server: Get city and station data in one call.
You no longer need to manually calculate the nearest city or look up the proper state/country code before querying. The agent handles the IP resolution via `get_nearest_city_by_ip` and provides the data immediately, regardless of the user's current network location.
The system understands the intent. You tell it what you want, and it executes the necessary location logic and data pull. It’s that simple.
Common Questions About AirVisual MCP
How do I use the `get_city_data` tool for a specific location? +
You pass the name of the city to the tool. This retrieves the current AQI and weather for that city. You can also use list_cities first to verify the spelling and support status.
What is the difference between `get_city_data` and `get_station_data`? +
get_city_data provides an overview for the whole city. get_station_data requires a specific station ID and gives deep, hyper-local readings from one point.
How does `get_nearest_city_by_ip` work? +
This tool automatically figures out the city closest to the user's IP address. It's useful when the user hasn't provided a location and you need immediate data.
Can I list all supported countries? Use `list_countries`. +
Yes, list_countries runs through the list of all countries AirVisual supports. This is useful for building global scope selectors into your application.
Does AirVisual MCP Server support coordinates? Use `get_nearest_city_by_coords`. +
Yes, you pass the latitude and longitude to get_nearest_city_by_coords. This finds the city nearest those coordinates and pulls the data.
How do I find nearby data using `get_nearest_city_by_coords`? +
It uses your exact GPS coordinates. You pass latitude and longitude to find the closest monitoring location. This is better than using IP lookup if you're working with precise coordinates.
How do I list available areas using `list_states`? +
First, you run list_countries to get the country code. Then, pass that code into list_states to get all supported states within that country. This gives you the necessary scope for city searches.
Can I get data for a specific station using `get_station_data`? +
Yes, you must provide the specific station ID. This tool gives you granular, hyper-local readings, which are much more detailed than general city averages.
Can I get air quality data for my current location without entering coordinates? +
Yes! You can use the get_nearest_city_by_ip tool. The agent will use your requester IP address to find and return the AQI and weather data for the closest supported city automatically.
How do I find out which cities are supported in a specific region? +
You can browse the hierarchy using list_countries, then list_states for a specific country, and finally list_cities for a specific state. This allows you to discover exactly which locations have active monitoring.
Does this server provide data from specific monitoring stations? +
Yes, the get_station_data tool allows you to retrieve real-time data from a specific named monitoring station if you know its name and location details (city, state, country).
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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