Agentic SPARQL: Evaluating SPARQL-MCP-powered Intelligent Agents on the Federated KGQA Benchmark

2026-01-20

AI Summary

This work proposes a novel architecture based on the Model Context Protocol (MCP) for complex question answering over multiple knowledge graphs, integrating SPARQL federated query capabilities into large language model agents for the first time. The approach employs a three-stage collaborative mechanism—endpoint discovery, schema exploration, and query generation—to enable automatic federated querying across heterogeneous knowledge graph sources. The study extends existing federated KGQA benchmarks and systematically evaluates the effectiveness of various agent designs, demonstrating that SPARQL-MCP achieves superior performance and exhibits strong practical potential in complex, multi-source question-answering scenarios.

Abstract

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.

Problem

• SPARQL
• MCP
• Federated Knowledge Graph Question Answering
• Intelligent Agents
• LLM

Innovation

• Agentic AI
• SPARQL federation
• Model Context Protocol (MCP)
• Federated KGQA
• LLM-based agents