Standard protocols such as the Model Context Protocol (MCP) that allow LLMs to connect to tools have recently boosted "agentic" AI applications, which, powered by LLMs' planning capabilities, promise to solve complex tasks with the access of external tools and data sources. In this context, publicly available SPARQL endpoints offer a natural connection to combine various data sources through MCP by (a) implementing a standardised protocol and query language, (b) standardised metadata formats, and (c) the native capability to federate queries. In the present paper, we explore the potential of SPARQL-MCP-based intelligent agents to facilitate federated SPARQL querying: firstly, we discuss how to extend an existing Knowledge Graph Question Answering benchmark towards agentic federated Knowledge Graph Question Answering (FKGQA); secondly, we implement and evaluate the ability of integrating SPARQL federation with LLM agents via MCP (incl. endpoint discovery/source selection, schema exploration, and query formulation), comparing different architectural options against the extended benchmark. Our work complements and extends prior work on automated SPARQL query federation towards fruitful combinations with agentic AI.

翻译：模型上下文协议（MCP）等标准化协议允许大语言模型（LLM）连接各类工具，近期显著推动了“智能体化”人工智能应用的发展——这些应用凭借LLM的规划能力，借助外部工具与数据源的访问，有望解决复杂任务。在此背景下，公开可用的SPARQL端点通过以下方式为通过MCP整合多源数据提供了天然连接：（a）实现标准化协议与查询语言，（b）采用标准化元数据格式，以及（c）具备原生联邦查询能力。本文旨在探索基于SPARQL-MCP的智能体在联邦SPARQL查询中的潜力：首先，我们探讨如何将现有知识图谱问答基准测试扩展至智能体化联邦知识图谱问答（FKGQA）；其次，我们通过MCP（包括端点发现/数据源选择、模式探索与查询公式化等环节）实现并评估SPARQL联邦与LLM智能体的集成能力，基于扩展基准测试对不同架构方案进行对比。本研究补充并延展了现有自动化SPARQL查询联邦研究，为实现该领域与智能体AI的深度融合提供了新路径。