LLM-enhanced Air Quality Monitoring Interface via Model Context Protocol

TL;DR

This paper introduces an LLM-enhanced air quality monitoring interface that integrates real-time sensor data with conversational AI via the Model Context Protocol, improving accessibility and reliability for non-expert users.

Contribution

It presents a novel system combining LLMs with a standardized tool protocol to create a reliable, interactive environmental monitoring interface grounded in live data.

Findings

High factual accuracy and completeness scores from expert evaluation

Minimal hallucinations demonstrated in system responses

Effective integration of LLMs with real-time environmental data

Abstract

Air quality monitoring is central to environmental sustainability and public health, yet traditional systems remain difficult for non-expert users to interpret due to complex visualizations, limited interactivity, and high deployment costs. Recent advances in Large Language Models (LLMs) offer new opportunities to make sensor data more accessible, but their tendency to produce hallucinations limits reliability in safety-critical domains. To address these challenges, we present an LLM-enhanced Air Monitoring Interface (AMI) that integrates real-time sensor data with a conversational interface via the Model Context Protocol (MCP). Our system grounds LLM outputs in live environmental data, enabling accurate, context-aware responses while reducing hallucination risk. The architecture combines a Django-based backend, a responsive user dashboard, and a secure MCP server that exposes system functions as discoverable tools, allowing the LLM to act as an active operator rather than a passive responder. Expert evaluation demonstrated high factual accuracy (4.78), completeness (4.82), and minimal hallucinations (4.84), on a scale of 5, supported by inter-rater reliability analysis. These results highlight the potential of combining LLMs with standardized tool protocols to create reliable, secure, and user-friendly interfaces for real-time environmental monitoring.