Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Nuansa Informatika
ANINDITO
Universitas Pertahanan
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Operationalizing Model Context Protocol for Multi-Platform Academic Search: A Comparative Study of LLM Grounding RIZQULLAH ARYAPUTRA PILIANG; ANINDITO; ERYAN AHMAD FIRDAUS
NUANSA INFORMATIKA Vol. 20 No. 1 (2026): Nuansa Informatika 20.1 Januari 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i1.513

Abstract

This study introduces the Paper Search Model Context Protocol (MCP) server, a unified architecture enabling Large Language Models (LLMs) to autonomously search and retrieve academic literature across heterogeneous platforms (arXiv, PubMed, bioRxiv, medRxiv, Semantic Scholar, and CrossRef). By normalizing diverse metadata into a standardized schema, the system facilitates platform-agnostic tool use. We evaluate this infrastructure by tasking GPT-4.1 and GPT-5.1 with generating grounded literature reviews on AI applications in medicine, cybersecurity, and transportation, measuring performance against retrieved abstracts using ROUGE and BERTScore. Results indicate that GPT-4.1 achieves superior semantic and structural alignment, particularly when grounded via CrossRef (BERTScore F1 = 0.881, ROUGE-L F1 = 0.375), outperforming GPT-5.1 across most metrics. While GPT-5.1 demonstrates higher unigram recall (ROUGE-1 = 0.412 on arXiv), it exhibits lower structural fidelity. These findings validate MCP as a robust integration layer for academic RAG systems and demonstrate that standardized tool interfaces enable precise, quantitative assessment of LLM grounding capabilities.
Co-Authors Eryan Ahmad Firdaus RIZQULLAH ARYAPUTRA PILIANG