# Responsible AI Access to Research Repositories: An MCP Server for African Studies Data

- **Type:** Poster presentation
- **Speakers:** Frédérick Madore, Oliver Baumann, Durgesh Nandini, Neeraj Thandayan Viswajith, Jiayu Yang and Mirco Schönfeld
- **Event:** AI Day 2026, Research Center for AI in Science and Society (RAIS²)
- **Location:** University of Bayreuth
- **Country:** Germany
- **Date:** 25 September 2026
- **Language:** English
- **Project:** [Digital Research Environment (University of Bayreuth)](https://www.frederickmadore.com/research/digital-research-environment.md)
- **Tags:** MCP, AMIRA, Africa Multiple, Large Language Models, AI, Data Sovereignty, Metadata, Digital Humanities, African Studies
- **Web page:** https://www.frederickmadore.com/communications/responsible-ai-access-mcp-server-2026

## 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.

## Links

- [Event Website](https://www.rais2.uni-bayreuth.de/en/events/ai_day_2026/index.html)
- [Poster (PDF)](https://www.frederickmadore.com/files/responsible-ai-access-mcp-server-2026-poster.pdf)