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Enhancing Dspace with Large Language Models: Designing an Integration Framework Using the Model Context Protocol

Authors: Dasgupta, Tirtharaj; Neogi, Madhumita; Mukhopadhyay, Parthasarathi;

Enhancing Dspace with Large Language Models: Designing an Integration Framework Using the Model Context Protocol

Abstract

This study aims to integrate Natural Language Processing (NLP) services within the DSpace-based repositories by using Model Context Protocol (MCP), a novel framework that promotes secured and seamless integration between LLM and external tools. This research study deployed Claude Sonnet as a front-end LLM and DSpace 9.1 as back-end digital repository software and connected them by using the MCP server to enable the natural language-based retrieval. This study highlights how MCP may allow context-aware NLP services to enhance metadata-driven retrieval and semantic search, as well as the multilingual information retrieval. It highlights the advantages of natural language-based retrieval and also identifies the limitations of this approach. This study reports developing a working prototype for natural language-based retrieval of DSpace and discusses the feasibility of this approach in real-world digital library settings, with a focus on large-scale national initiatives such as the National Digital Library of India (NDLI).

Related Organizations
Keywords

Large Language Model (LLM), Information Retrieval, Natural Language Processing (NLP), NDLI, Model Context Protocol (MCP), Claude, DSpace

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green