@prefix rdf: . @prefix rdfs: . @prefix xsd: . @prefix schema: . @prefix void: . @prefix dcterms: . @prefix foaf: . @prefix bibo: . @prefix fabio: . a fabio:ConferencePaper ; rdfs:label "Responsible AI Access to Research Repositories: An MCP Server for African Studies Data" ; dcterms:subject , , , , , , , , ; dcterms:title "Responsible AI Access to Research Repositories: An MCP Server for African Studies Data" ; dcterms:date "2026-09-25"^^xsd:date ; bibo:abstract "As large language models (LLMs) increasingly mediate access to digital repositories, institutions face a choice between blocking AI access entirely and surrendering their data to unrestricted extraction. We argue for a third position, mediated openness, and demonstrate it through an open-source Model Context Protocol (MCP) server built for the Africa Multiple Interactive Research Atlas (AMIRA), the multilingual research-data platform for African studies developed within the “Africa Multiple” Cluster of Excellence. The server lets conversational AI assistants query AMIRA’s metadata on the institution’s own terms: read-only, provenance-preserving, and governed by the underlying data reconciliation infrastructure rather than by model inference. Twenty-six tools cover structured search, record retrieval, full-text and transcript search, and relational discovery; the signature tool, FindRelated, operationalises co-occurrence across people, places, subjects, projects and formats, turning a static metadata graph into something an LLM can reason over responsibly. We evaluate the system along the two axes most relevant to trustworthy AI-mediated research access: response stability and source fidelity across repeated sessions. Identical queries posed in independent sessions returned identical, correctly ordered result sets, and even a misspelled query was resolved correctly rather than producing a fabricated answer. MCP does not resolve the political economy of AI data extraction, but it offers institutions a concrete, sovereignty-preserving architecture that keeps governance, provenance and epistemic authority with the data holder rather than the model."@en ; dcterms:language ; dcterms:identifier "eref-99712" ; bibo:uri ; dcterms:type ; bibo:status ; dcterms:relation ; bibo:presentedAt "AI Day 2026, University of Bayreuth, 25 September 2026" ; bibo:authorList ; dcterms:creator , , , , , ; schema:isPartOf ; foaf:isPrimaryTopicOf . rdf:first ; rdf:rest . rdf:first ; rdf:rest . rdf:first ; rdf:rest . rdf:first ; rdf:rest . rdf:first ; rdf:rest . rdf:first ; rdf:rest rdf:nil . dcterms:modified "2026-10-19T10:15:14Z"^^xsd:dateTime . foaf:primaryTopic ; void:inDataset ; dcterms:modified "2026-10-19T10:15:14Z"^^xsd:dateTime ; dcterms:license .