PluginBench
MCP Server
Active
Apache-2.0

NVIDIA Elements MCP Server

io.github.NVIDIA/elements

NVIDIA's design system and UI agent tools for AI/ML, robotics, and autonomous vehicle applications.

What is the NVIDIA Elements MCP server?

NVIDIA Elements is a design system and MCP server that exposes component APIs, design tokens, examples, and validation tools for building operational UIs in AI/ML factories, robotics, and autonomous vehicles. It provides agent-ready tooling via CLI and MCP, with framework-agnostic Web Components that work across React, Angular, Vue, Svelte, Lit, and plain HTML.

NVIDIA Elements is a design system built for AI infrastructure with agent-ready tooling. It exposes component APIs, design tokens, examples, imports, and validation through CLI and MCP interfaces. The system is framework-agnostic, supporting React, Angular, Vue, Svelte, Lit, and server-rendered templates, making it suitable for building operational UIs for AI/ML workloads, autonomous vehicles, and robotics consoles.

How to install NVIDIA Elements

Copy-paste configuration for popular MCP clients.

transport: stdio
Config generated by PluginBench — verify against the source before use.
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "elements": {
      "command": "npx",
      "args": [
        "-y",
        "@nvidia-elements/cli",
        "mcp"
      ]
    }
  }
}

Tools & capabilities

Tools this server exposes to the agent.

  • Component APIs — Access to curated UI components maintained by the Elements team
  • Design Tokens — Design system tokens for consistent styling and theming
  • Validation — Automated static analysis and linting for UI authoring best practices
  • Examples & Imports — Pre-built examples and component import utilities
  • Setup Tools — CLI and MCP tooling for project initialization and configuration

Use cases

  • Build operational dashboards for AI/ML workloads with standardized components
  • Create autonomous vehicle control interfaces using framework-agnostic Web Components
  • Develop robotics console UIs with validated design patterns and tokens
  • Scaffold new projects with standardized starter applications
  • Validate UI code against NVIDIA design best practices with automated linting

NVIDIA Elements MCP server FAQ

What is NVIDIA Elements?

NVIDIA Elements is a design system and MCP server providing agent-ready UI tooling for AI/ML, robotics, and autonomous vehicle applications. It exposes component APIs, design tokens, examples, and validation through CLI and MCP interfaces.

Is NVIDIA Elements free?

Yes, NVIDIA Elements is open-source and available on GitHub at https://github.com/NVIDIA/elements.

How do I install NVIDIA Elements?

Install via npm with `npm install @nvidia-elements/cli` or use the skills CLI: `npx skills add https://github.com/nvidia/elements --skill elements`.

What frameworks does NVIDIA Elements support?

NVIDIA Elements is framework-agnostic and works with React, Angular, Vue, Svelte, Lit, plain HTML, server-rendered templates, and mixed stacks.

Does NVIDIA Elements require authentication?

No authentication is required. NVIDIA Elements is an open-source design system available for public use.

What kind of projects is NVIDIA Elements designed for?

NVIDIA Elements is built for operational UIs in AI/ML factories, autonomous vehicle tools, and robotics consoles, with stable API contracts and automated validation.

README (reference)

Source of truth, from the repository.

NVIDIA Elements

NVIDIA Design System and UI Agent Harness for AI/ML Factories, Robotics, and Autonomous Vehicles.

  • Agent-ready tooling: CLI and MCP expose component APIs, tokens, examples, imports, validation, and setup to terminals and AI assistants.
  • Framework agnostic: Web Components run in React, Angular, Vue, Svelte, Lit, plain HTML, server-rendered templates, and mixed stacks.
  • Built for AI infrastructure: Operational UI for AI/ML workloads, autonomous vehicle tools, and robotics consoles.
  • Stable API contracts: Skills and lint guide authoring best practices, common UI patterns, and automated static analysis.

NVIDIA Elements Skill

Install the Elements agent skill with the open skills CLI:

npx skills add https://github.com/nvidia/elements --skill elements

Requests and Contributions

Organization

The repository uses a top-level repository with individual project directories.

Project directories have their own package.json and commands. Run CI and development setup at the root repository level.

Examples of projects include:

  • /projects/starters - Suite of standardized starter apps for Elements and Patterns
  • /projects/core - Elements library: curated UI maintained by the Elements team
  • /projects/themes - Elements Theme library: provides a set of supported themes for Element based projects
  • /projects/styles - Elements Styles library: provides a set of CSS utilities for layout and typography

Development

Setup

To set up repository dependencies and run the full build, run the following commands at the root of the repository:

The CI pipeline also builds Go starters. Install Go 1.26.x before running the full local CI pipeline.

# install dependencies https://mise.en.dev/getting-started.html
curl https://mise.run | sh
~/.local/bin/mise run setup

Troubleshooting

If you are coming from development from a different repository, you may need to refresh the repository toolchain with mise. Run mise run install at the root of the project to install the Node.js, pnpm, Vale, Go, Git LFS, and package versions from mise.toml and pnpm-lock.yaml.

If you actively switch between different repositories, run commands through mise exec -- or activate mise in your shell so it switches tools automatically.

Building

Both the top-level repository and each project have a set of standardized npm scripts. To build and test all projects, run mise exec -- pnpm run ci at the root of the repository.

Top-Level Repository

  • ci: run full build/lint/test
  • ci:all: entire CI process: build, lint, unit/lighthouse/visual tests
  • ci:reset: clear all caches/dependencies then reinstall dependencies

Individual Projects

Common project scripts include:

  • dev: run in watch mode
  • build: run project/library build
  • test: run unit tests
  • test:lighthouse: run lighthouse performance tests
  • test:visual: run playwright visual regression tests
  • test:axe: run axe tests for a11y

The available scripts vary by project. Check the project's package.json before running project-specific commands.

For details about the repository build and runtime flow, see the build system documentation.

Workflow

Before creating a branch or pull request, make a new issue or feature request first so the team can check alignment and avoid duplicate work.

Create a Branch

Use a descriptive branch name with the topic/ prefix. Example topic/bug-fix.

git checkout -b topic/bug-fix

After creating your branch, make your source code changes. After you complete them, run mise exec -- pnpm run ci in the root of the repo to run all the builds and tests. If all tests pass, you are ready to create a PR.

Commit Messages

The repo uses Semantic Release to manage package changes. Commit messages determine the release on merge. Commit Lint enforces commit formatting.

git commit -a -s -m "fix(core): disable multi-select"

Example Commit

TypesDescription
fixbug fixes, performance fixes
featnew features, components, APIs
chorenon production code modifications, build tooling, documentation
ScopesDescription
ciCI and release automation
cli/projects/cli
code/projects/code
core/projects/core
create/projects/create
depsdependency updates
docsdocumentation and site content
forms/projects/forms
internals/projects/internals
lint/projects/lint
markdown/projects/markdown
media/projects/media
monaco/projects/monaco
pages/projects/pages
pi/projects/pi
starters/projects/starters
styles/projects/styles
themes/projects/themes

Keep commit names focused on your changes. The release process uses the commit message to determine the next release and generated changelog notes.

Opening a Pull Request

Once you have committed your changes to your branch locally, push them to the remote GitHub repository.

git push --set-upstream origin topic/bug-fix

Open a new Pull Request in GitHub. Request review from the team members and apply the appropriate labels in the GitHub UI, for example, type:fix and scope(core).

Amending Commit

If there are changes requested, make the requested changes locally and amend the commit.

git commit -a --amend --no-edit

This adds the changes to your existing commit. Then push the updated commit back to the remote branch for review.

git push --force origin topic/bug-fix

Rebasing Commit

If main changes before PR approval, rebase your local branch to include the latest changes from main.

git checkout main # Switch to main branch
git pull # Pull down any new changes
git checkout topic/bug-fix # Switch back to your topic branch
git rebase main # Rebase your branch onto the latest main

You may have to resolve any merge conflicts that arise from this process. Once complete, push the updated branch back to the remote repository for review.

New Project

When creating a new project, ex: ./projects/code, make sure to add the project to the pnpm-workspace.yaml located at the root directory.

Release

After approval, merge your Pull Request into main through the GitHub UI. GitHub Actions triggers a new release automatically. Semantic Release calculates the version from the commit type. The changelog also includes the commits from the PR.

Related MCP servers

AI that remembers. Local-first cross-session, cross-project long-term memory for coding tools.

5
TypeScript
View repository →

Unofficial read-only MCP server for your local WhatsApp chat history (stays on your machine).

0
Python
MIT
View repository →

MCP server exposing 104+ modern CLI tools for AI agents with JSON output.

View repository →

Search, compare, and get pricing for 8,600+ software tools with verified data.

3
TypeScript
MIT
View repository →

Give your AI agent Swift reviews, local Apple references, app scaffolding and iOS simulator workflows.

37
HTML
MIT
View repository →
PDpdfmux logo

pdfmux

Active

PDF-to-Markdown extraction that audits its own output and flags silent drops across any extractor.

72
Python
MIT
View repository →