viznoir

viznoir

VTK is all you need. Cinema-quality science visualization for AI agents.

*One prompt → physics analysis → cinematic renders → LaTeX equations → publication-ready story.*

What it does

An MCP server that gives AI agents full access to VTK's rendering pipeline — no ParaView GUI, no Jupyter notebooks, no display server. Your agent reads simulation data, applies filters, renders cinema-quality images, and exports animations, all headless.

Works with: Claude Code · Cursor · Windsurf · Gemini CLI · any MCP client

Quick Start

1. Install

pip install viznoir

With optional extras

pip install "viznoir[mesh]" # meshio + trimesh (50+ formats) pip install "viznoir[composite]" # Pillow + matplotlib (split_animate) pip install "viznoir[all]" # everything

Requires Python ≥3.10. VTK wheel auto-installed (EGL headless rendering supported).

2. Verify

mcp-server-viznoir --help    # server entry point

python -c "import viznoir; print(viznoir.version)"

3. Use with an MCP client

Add to your MCP client config (claude_desktop_config.json, ~/.cursor/mcp.json, etc.):

{

"mcpServers": { "viznoir": { "command": "mcp-server-viznoir", "env": { "VIZNOIR_DATA_DIR": "/path/to/your/simulation/data", "VIZNOIR_OUTPUT_DIR": "/path/to/output" } } } }

Then ask your AI agent:

*"Open cavity.foam, render the pressure field with cinematic lighting, then create a physics decomposition story."*

4. Or use as a Python library (advanced)

All tool implementations are importable as async functions. You provide a VTKRunner and await the result:

import asyncio

from viznoir.core.runner import VTKRunner from viznoir.tools.inspect import inspect_data_impl from viznoir.tools.render import render_impl

async def main(): runner = VTKRunner()

meta = await inspect_data_impl(file_path="cavity.foam", runner=runner) print(meta["fields"], meta["timesteps"])

result = await render_impl( file_path="cavity.foam", field_name="p", runner=runner, colormap="Cool to Warm", camera="isometric", width=1920, height=1080, output_filename="pressure.png", ) print(result.file_path)

asyncio.run(main())

See docs for the full tool reference.

Capabilities

22 tools · 12 resources · 4 prompts · 50+ file formats (OpenFOAM, VTK, CGNS, Exodus, STL, glTF, …)

Showcase — 10 Domains, One Pipeline

Every frame below is a single MCP tool call. No GUI, no post-processing, no ParaView. Annotations are rendered inside the 3D scene via VTK-native text actors and leader lines — no Photoshop, no matplotlib overlay.

| | | | | | |:-:|:-:|:-:|:-:|:-:| | | | | | | | Medical CT skull volume | CFD Combustion streamlines | Thermal Heatsink gradient | Geoscience Seismic wavefield | Automotive DrivAerML · 8.8M cells | | | | | | | | Molecular H₂O electron density | Vascular Cerebral aneurysm MRA | Planetary Bennu · 196K triangles | Structural Cantilever FEA stress | Volume Thermal threshold |

Physics-Aware Animations

Seven presets convert raw simulation data into publication-ready motion — each binds a rendering primitive to a physical phenomenon.

Story Composition (compose_assets)

*Inspect → render → annotate → compose → narrate. One prompt produces a 4-panel physics decomposition with LaTeX-rendered governing equations.*

Layouts: story (vertical narrative) · grid (N×M comparison) · slides (16:9 keynote) · video (MP4 with transitions)

Full interactive gallery: https://kimimgo.github.io/viznoir/#showcase

Architecture

  prompt                    "Render pressure from cavity.foam"

│ MCP Server 22 tools · 12 resources · 4 prompts │ VTK Engine readers → filters → renderer → camera │ EGL/OSMesa headless · cinematic lighting Physics Layer topology analysis · context parsing │ vortex detection · stagnation points Animation 7 physics presets · easing · timeline │ transitions · compositor · video export Output PNG · WebP · MP4 · GLTF · LaTeX

Numbers

Documentation

Homepage: kimimgo.github.io/viznoir

Developer docs: kimimgo.github.io/viznoir/docs — full tool reference, domain gallery, architecture guide

License

MIT