Air Quality Analytics
Full-stack air quality analytics platform built with FastAPI, React, and MySQL. Aggregates multi-source PM2.5/PM10 data, performs multi-city comparison and time-series forecasting (SARIMAX), and integrates an LLM-based planning agent with tiered acce
What is Air Quality Analytics?
Air Quality Analytics is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to full-stack air quality analytics platform built with fastapi, react, and mysql. aggregates multi-source pm2.5/pm10 data, performs multi-city comparison and time-series forecasting (sarimax), and integ...
Full-stack air quality analytics platform built with FastAPI, React, and MySQL. Aggregates multi-source PM2.5/PM10 data, performs multi-city comparison and time-series forecasting (SARIMAX), and integrates an LLM-based planning agent with tiered acce
This server falls under the Data Science & ML category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Full-stack air quality analytics platform built with FastAPI
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx air-quality-trends-analysis-projectConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Air Quality Analytics
Air Quality Analytics is a full-stack platform that aggregates PM2.5 and PM10 data from multiple sources including Open-Meteo, OpenAQ, IQAir, and WAQI, performs multi-city comparative analysis, and applies SARIMAX time-series forecasting to predict pollution trends. It ships an LLM-based planning agent with a critic-based reflection pattern that converts natural-language queries into data pipeline steps, and exposes the results through a FastAPI backend and React frontend. Researchers and environmental data engineers use it to automate air quality monitoring workflows and generate PDF reports.
Prerequisites
- Python 3.10 or later and pip installed
- Node.js 18 or later (for the React frontend)
- MySQL database server running locally or remotely
- API keys for IQAir and WAQI if using authenticated data sources
- An MCP-compatible client such as Claude Desktop
Clone the repository
Download the Air Quality project source code from GitHub.
git clone https://github.com/dyneth02/Air-Quality-Trends-Analysis-Project.git
cd Air-Quality-Trends-Analysis-ProjectInstall Python backend dependencies
Install the FastAPI backend dependencies including the MCP server components and forecasting libraries.
pip install -r requirements.txtConfigure your database and API credentials
Set up MySQL connection details and any required data-source API keys in your environment or a .env file.
DATABASE_URL=mysql+pymysql://user:password@localhost:3306/air_quality
IQAIR_API_KEY=your_iqair_key_here
WAQI_API_KEY=your_waqi_key_hereStart the FastAPI backend
Launch the backend server which exposes the MCP endpoint and REST API for data access.
uvicorn main:app --host 0.0.0.0 --port 8000 --reloadInstall and start the React frontend
Install frontend dependencies and start the development server to access the visualization dashboard.
cd frontend
npm install
npm run devConnect Claude Desktop to the MCP endpoint
Register the Air Quality MCP server in your claude_desktop_config.json to enable natural-language queries against the platform.
{
"mcpServers": {
"air-quality": {
"command": "python",
"args": ["-m", "mcp_server"],
"env": {
"DATABASE_URL": "mysql+pymysql://user:password@localhost:3306/air_quality"
}
}
}
}Air Quality Analytics Examples
Client configuration
claude_desktop_config.json entry for the Air Quality Analytics MCP server.
{
"mcpServers": {
"air-quality": {
"command": "python",
"args": ["-m", "mcp_server"],
"env": {
"DATABASE_URL": "mysql+pymysql://user:password@localhost:3306/air_quality"
}
}
}
}Prompts to try
Example natural-language queries for air quality analysis and forecasting.
- "Compare PM2.5 levels in New York, Los Angeles, and Chicago over the last 30 days"
- "Forecast PM10 pollution in Delhi for the next two weeks using historical trends"
- "Show me which city had the worst air quality this month and generate a PDF report"
- "What are the mean, min, and max PM2.5 values for Beijing in 2024?"
- "Plot a time series of PM2.5 data for London and highlight anomalies"Troubleshooting Air Quality Analytics
Database connection fails on startup
Verify that MySQL is running and that DATABASE_URL uses the correct host, port, username, password, and database name. Run 'mysql -u user -p air_quality' to test credentials independently.
SARIMAX forecasting raises a 'convergence warning' or fails
SARIMAX requires sufficient historical data. Ensure at least 60 days of data are present in the database for the target city before running forecasting queries.
The LLM planning agent returns 'I cannot complete this query'
The agent uses a critic-based reflection pattern and may reject ambiguous queries. Rephrase with explicit city names, date ranges, and pollutant types (PM2.5 or PM10) to give the planner enough context.
Frequently Asked Questions about Air Quality Analytics
What is Air Quality Analytics?
Air Quality Analytics is a Model Context Protocol (MCP) server that full-stack air quality analytics platform built with fastapi, react, and mysql. aggregates multi-source pm2.5/pm10 data, performs multi-city comparison and time-series forecasting (sarimax), and integrates an llm-based planning agent with tiered acce It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Air Quality Analytics?
Follow the installation instructions on the Air Quality Analytics GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Air Quality Analytics?
Air Quality Analytics works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Air Quality Analytics free to use?
Yes, Air Quality Analytics is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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