Publication Date
9-11-2025
Document Type
Article
Publication Title
Journal of Marine Science and Engineering
Volume
13
Issue
9
DOI
10.3390/jmse13091754
Abstract
Ports and maritime operations generate massive real-time data streams, particularly from Automatic Identification System (AIS) signals, which are challenging to query effectively using natural language. This study proposes a prototype AISStream-MCP, a memory-augmented real-time maritime question-answering (QA) system that integrates live AIS data streaming with a Model Context Protocol (MCP) toolchain to support port operations decision-making. The system combines a large language model (LLM) with four MCP-enabled modules: persistent dialogue memory, live AIS data query, knowledge graph lookup, and result evaluation. We hypothesize that augmenting an LLM with domain-specific tools significantly improves QA performance compared to systems without memory or live data access. To test this hypothesis, we developed two prototype systems (with and without MCP framework) and evaluated them on 30 queries across three task categories: ETA prediction, anomaly detection, and multi-turn route queries. Experimental results demonstrate that AISStream-MCP achieves 88% answer accuracy (vs. 75% baseline), 85% multi-turn coherence (vs. 60%), and 38.7% faster response times (4.6 s vs. 7.5 s), with user satisfaction scores of 4.6/5 (vs. 3.5/5). The improvements are statistically significant (p < 0.01), confirming that memory augmentation and real-time tool integration effectively enhance maritime QA capabilities. Specifically, AISStream-MCP improved ETA prediction accuracy from 80% to 90%, anomaly detection from 70% to 85%, and multi-turn query accuracy from 65% to 88%. This approach shows significant potential for improving maritime situational awareness and operational efficiency.
Funding Number
2020YFC1522604-01
Funding Sponsor
National Key Research and Development Program of China
Keywords
knowledge augmentation, maritime question-answering, model context protocol, port decision support, real-time AIS data
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Hospitality, Tourism, and Event Management
Recommended Citation
Sien Chen, Ruoxian Zhao, Jian Bo Yang, and Yinghua Huang. "AISStream-MCP: A Real-Time Memory-Augmented Question-Answering System for Maritime Operations" Journal of Marine Science and Engineering (2025). https://doi.org/10.3390/jmse13091754
