VACP: Visual Analytics Context Protocol
Abstract
The rise of AI agents introduces a fundamental shift in Visual Analytics (VA), in which agents act as a new user group. Current agentic approaches - based on computer vision and raw DOM access - fail to perform VA tasks accurately and efficiently. This paper introduces the Visual Analytics Context Protocol (VACP), a framework designed to make VA applications "agent-ready" that extends generic protocols by explicitly exposing application state, available interactions, and mechanisms for direct execution. To support our context protocol, we contribute a formal specification of AI agent requirements and knowledge representations in VA interfaces. We instantiate VACP as a library compatible with major visualization grammars and web frameworks, enabling augmentation of existing systems and the development of new ones. Our evaluation across representative VA tasks demonstrates that VACP-enabled agents achieve higher success rates in interface interpretation and execution compared to current agentic approaches, while reducing token consumption and latency. VACP closes the gap between human-centric VA interfaces and machine perceivability, ensuring agents can reliably act as collaborative users in VA systems.
Keywords
Cite
@article{arxiv.2603.29322,
title = {VACP: Visual Analytics Context Protocol},
author = {Tobias Stähle and Péter Ferenc Gyarmati and Thilo Spinner and Rita Sevastjanova and Dominik Moritz and Mennatallah El-Assady},
journal= {arXiv preprint arXiv:2603.29322},
year = {2026}
}