# ParaView-MCP: An Autonomous Visualization Agent with Direct Tool Use

arXiv: https://arxiv.org/abs/2505.07064 · HTML: https://mlsanity.com/paper/2505.07064/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 11 May 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 22 of 22)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)](https://mlsanity.com/paper/2309.17421/) (2023)
- [Agentic Large Language Models, a survey](https://mlsanity.com/paper/2503.23037/) (2025)
- [MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization](https://mlsanity.com/paper/2402.11453/) (2024)
- [NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries](https://mlsanity.com/paper/2008.10723/) (2020)
- [Compressive Neural Representations of Volumetric Scalar Fields](https://mlsanity.com/paper/2104.04523/) (2021)
- [VizML: A Machine Learning Approach to Visualization Recommendation](https://mlsanity.com/paper/1808.04819/) (2018)
- [AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization](https://mlsanity.com/paper/2102.01330/) (2021)
- [HAIChart: Human and AI Paired Visualization System](https://mlsanity.com/paper/2406.11033/) (2024)
- [LLM4Vis: Explainable Visualization Recommendation using ChatGPT](https://mlsanity.com/paper/2310.07652/) (2023)
- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow](https://mlsanity.com/paper/2502.05036/) (2025)
- [Towards Natural Language-Based Visualization Authoring](https://mlsanity.com/paper/2208.10947/) (2022)
- [AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making](https://mlsanity.com/paper/2312.04494/) (2023)
- [Exploring the Capability of LLMs in Performing Low-Level Visual Analytic Tasks on SVG Data Visualizations](https://mlsanity.com/paper/2404.19097/) (2024)
- [DracoGPT: Extracting Visualization Design Preferences from Large Language Models](https://mlsanity.com/paper/2408.06845/) (2024)
- [ChatVis: Automating Scientific Visualization with a Large Language Model](https://mlsanity.com/paper/2410.11863/) (2024)
- [See or Recall: A Sanity Check for the Role of Vision in Solving Visualization Question Answer Tasks with Multimodal LLMs](https://mlsanity.com/paper/2504.09809/) (2025)
- [Text-based Transfer Function Design for Semantic Volume Rendering](https://mlsanity.com/paper/2406.15634/) (2024)
- [ChatGPT in Data Visualization Education: A Student Perspective](https://mlsanity.com/paper/2405.00748/) (2024)
- [The Visualization JUDGE : Can Multimodal Foundation Models Guide Visualization Design Through Visual Perception?](https://mlsanity.com/paper/2410.04280/) (2024)

## Built on by (most cited first, 11 of 11)

- [NLI4VolVis: Natural Language Interaction for Volume Visualization via LLM Multi-Agents and Editable 3D Gaussian Splatting](https://mlsanity.com/paper/2507.12621/) (2025)
- [SciVisAgentBench: A Benchmark for Evaluating Scientific Data Analysis and Visualization Agents](https://mlsanity.com/paper/2603.29139/) (2026)
- [An Evaluation-Centric Paradigm for Scientific Visualization Agents](https://mlsanity.com/paper/2509.15160/) (2025)
- [InferA: A Smart Assistant for Cosmological Ensemble Data](https://mlsanity.com/paper/2510.12920/) (2025)
- [IntuiTF: MLLM-Guided Transfer Function Optimization for Direct Volume Rendering](https://mlsanity.com/paper/2506.18407/) (2025)
- [TopoPilot: Reliable Conversational Workflow Automation for Topological Data Analysis and Visualization](https://mlsanity.com/paper/2603.25063/) (2026)
- [Toward AI VIS Co-Scientists: A General and End-to-End Agent Harness for Solving Complex Data Visualization Tasks](https://mlsanity.com/paper/2605.21825/) (2026)
- [Exploring LLM Agent Designs and Interaction Modalities for Scientific Visualization](https://mlsanity.com/paper/2604.27996/) (2026)
- [HiLSVA: Design and Evaluation of a Human-in-the-Loop Agentic System for Scientific Visualization](https://mlsanity.com/paper/2606.26614/) (2026)
- [SciVisAgentSkills: Design and Evaluation of Agent Skills for Scientific Data Analysis and Visualization](https://mlsanity.com/paper/2606.05525/) (2026)
- [Multi-Agent Discovery and Resource-Aware Autonomous Exploration of Scientific Datasets](https://mlsanity.com/paper/2608.22045/) (2026)

## Abstract (arXiv)

While powerful and well-established, tools like ParaView present a steep learning curve that discourages many potential users. This work introduces ParaView-MCP, an autonomous agent that integrates modern multimodal large language models (MLLMs) with ParaView to not only lower the barrier to entry but also augment ParaView with intelligent decision support. By leveraging the state-of-the-art reasoning, command execution, and vision capabilities of MLLMs, ParaView-MCP enables users to interact with ParaView through natural language and visual inputs. Specifically, our system adopted the Model Context Protocol (MCP) - a standardized interface for model-application communication - that facilitates direct interaction between MLLMs with ParaView's Python API to allow seamless information exchange between the user, the language model, and the visualization tool itself. Furthermore, by implementing a visual feedback mechanism that allows the agent to observe the viewport, we unlock a range of new capabilities, including recreating visualizations from examples, closed-loop visualization parameter updates based on user-defined goals, and even cross-application collaboration involving multiple tools. Broadly, we believe such an agent-driven visualization paradigm can profoundly change the way we interact with visualization tools. We expect a significant uptake in the development of such visualization tools, in both visualization research and industry.