Air Pollution MCP Server

Overview

The Air‑Pollution MCP server provides a lightweight, secure interface for retrieving real‑time air quality information from the OpenWeather API. By exposing a small set of endpoints that accept geographic coordinates or city/country pairs, the server abstracts away the intricacies of API authentication and request handling. This allows AI assistants to query air pollution metrics without needing direct access to the OpenWeather service or embedding secret keys in client code.

Problem Solved
Many AI‑driven applications require up‑to‑date environmental data to inform user decisions—whether for health advisories, smart city dashboards, or travel planning. Directly integrating with the OpenWeather API can be cumbersome: developers must manage API keys, handle rate limits, and parse JSON responses. The Air‑Pollution MCP server centralizes these concerns into a single, reusable service that can be deployed behind a firewall or within an existing micro‑service architecture.

Core Functionality & Value

• Secure Key Management: API credentials are stored in environment variables via , preventing accidental exposure.
• Flexible Querying: Clients can request data by latitude/longitude or by city and country, accommodating both programmatic and user‑friendly use cases.
• Efficient Request Handling: Express middleware streamlines request validation and error handling, ensuring consistent responses and reducing latency.
• MCP Compatibility: The server exposes resources, tools, prompts, and sampling endpoints in the Model Context Protocol format, enabling seamless interaction with Claude or other MCP‑compliant assistants.

Key Features Explained

• Resource Endpoints: Return structured air quality indices (e.g., PM2.5, PM10, Ozone) along with timestamps and location metadata.
• Tool Integration: The server can be invoked as a tool within an AI workflow, allowing the assistant to fetch fresh data on demand.
• Prompt Templates: Pre‑defined prompts guide users in constructing queries, improving the reliability of client requests.
• Sampling Controls: Optional parameters let developers limit data granularity or frequency, helping to stay within API quotas.

Use Cases & Real‑World Scenarios

• Health Advisory Bots: A medical chatbot can ask the MCP server for current pollution levels and recommend outdoor activity restrictions.
• Smart Home Automation: Voice assistants can trigger HVAC adjustments when air quality drops below a threshold.
• Travel Planning Apps: Users receive notifications about high pollution days in destination cities, influencing itinerary choices.
• Environmental Research: Data scientists can batch‑query air quality across multiple coordinates for trend analysis, all orchestrated through an AI pipeline.

Integration with AI Workflows
In a typical workflow, the AI assistant receives a user request containing location details. It then calls the MCP server’s tool endpoint, parses the returned JSON, and incorporates the data into its response or downstream actions. Because the server follows MCP conventions, developers can add it to existing tool registries with minimal configuration, leveraging standard authentication and error handling patterns already familiar from other MCP services.

Unique Advantages
Unlike generic weather APIs, this server focuses solely on air pollution metrics, providing a streamlined and purpose‑built interface. Its Node.js + Express foundation ensures high performance and easy scalability, while the explicit separation of concerns (API key handling, request routing, MCP compliance) makes it a drop‑in component for any AI‑powered application that needs reliable environmental data.