viznoir

Name: viznoir
Author: kimimgo

by kimimgoversion 0.10.1i★ –iRuns on your computer

Registry nameio.github.kimimgo/viznoir

Install

Overview

Description from the maker (original English)

Cinema-quality science visualization for AI agents — headless VTK via MCP.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiVery new↓ 23/wk · ★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: VIZNOIR_RENDER_BACKEND, VIZNOIR_OUTPUT_DIR

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "viznoir": {
      "command": "uvx",
      "args": [
        "viznoir"
      ],
      "env": {
        "VIZNOIR_RENDER_BACKEND": "your-value",
        "VIZNOIR_OUTPUT_DIR": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add viznoir --env VIZNOIR_RENDER_BACKEND=your-value --env VIZNOIR_OUTPUT_DIR=your-value -- uvx viznoir

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "viznoir": {
      "command": "uvx",
      "args": [
        "viznoir"
      ],
      "env": {
        "VIZNOIR_RENDER_BACKEND": "your-value",
        "VIZNOIR_OUTPUT_DIR": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "viznoir": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "viznoir"
      ],
      "env": {
        "VIZNOIR_RENDER_BACKEND": "your-value",
        "VIZNOIR_OUTPUT_DIR": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

VIZNOIR_RENDER_BACKEND env
Rendering backend: gpu, cpu, or auto
VIZNOIR_OUTPUT_DIR env
Output directory for rendered images

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account kimimgo, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 18474 servers in this category
Published to the registry more recently than 9% of the other 18474 servers in this category
Basic checks equal to or better than 100% of the other 18474 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is viznoir safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.10.1) reached the registry 5 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Thalovant MCP

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Spec Driven Development

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

by formulahendryEarly↓ 66/wk★ 440Updated 14 months ago

Runs on your computer

Install

Remote Desktop Commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

by desktop-commanderGrowing★ 211Updated 3 days ago

Runs in the cloud✓ desktopcommander.app

Install

Thalovant MCP

Connect MCP clients to Thalovant control-plane and hub runtime APIs over stdio or Streamable HTTP.

by thalovantEarly↓ 455/wkUpdated today

Runs on your computer

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Category	Tools
Rendering	`render` · `cinematic_render` · `batch_render` · `volume_render`
Filters	`slice` · `contour` · `clip` · `streamlines` · `pv_isosurface`
Analysis	`inspect_data` · `inspect_physics` · `extract_stats` · `analyze_data`
Probing	`plot_over_line` · `integrate_surface` · `probe_timeseries`
Animation	`animate` · `split_animate`
Comparison	`compare` · `compose_assets`
Export	`preview_3d` · `execute_pipeline`

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.

Physics-Aware Animations

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

Preset	Physics	Rendering
`streamline_growth`	Lagrangian advection	Particle path-line extension over time
`clip_sweep`	Pressure gradient cross-section	Moving clip plane
`layer_reveal`	CT density classification	Progressive isosurface stacking
`iso_sweep`	Orbital topology	Isovalue sweep with camera orbit
`warp_oscillation`	Structural mode shape	Warp-by-vector harmonic displacement
`light_orbit`	Oblique illumination	Rotating key light for material reveal
`threshold_reveal`	Feature hierarchy	Threshold peeling from outside → in

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


22 MCP tools	24 VTK filters
10 domains	19 native file formats
6/6 VTK data types	50+ formats via meshio

Documentation

Homepage: kimimgo.github.io/viznoir

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

License

MIT

viznoir

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Thalovant MCP

README from the maker

viznoir

What it does

Quick Start

1. Install

With optional extras

2. Verify

3. Use with an MCP client

4. Or use as a Python library (advanced)

Capabilities

Showcase — 10 Domains, One Pipeline

Physics-Aware Animations

Story Composition (compose_assets)

Architecture

Numbers

Documentation

License

Story Composition (`compose_assets`)