PatternFly MCP Server▌
by patternfly
Access PatternFly documentation and React docs, component schemas, and dev rules via PatternFly MCP Server — AI-assisted
Provides access to PatternFly React documentation, development rules, and component schemas through MCP tools, enabling AI assistants to help developers build applications with PatternFly components following best practices.
best for
- / React developers using PatternFly design system
- / Teams building enterprise applications with PatternFly
- / Developers learning PatternFly component APIs
capabilities
- / Retrieve PatternFly component documentation and schemas
- / Search PatternFly components by name
- / Access development rules and best practices
- / Get component JSON schemas for validation
what it does
Provides PatternFly React component documentation, schemas, and development best practices to AI assistants. Helps developers build applications using PatternFly components correctly.
about
PatternFly MCP Server is an official MCP server published by patternfly that provides AI assistants with tools and capabilities via the Model Context Protocol. Access PatternFly documentation and React docs, component schemas, and dev rules via PatternFly MCP Server — AI-assisted It is categorized under developer tools, design. This server exposes 3 tools that AI clients can invoke during conversations and coding sessions.
how to install
You can install PatternFly MCP Server in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
PatternFly MCP Server is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
PatternFly MCP Server
A Model Context Protocol (MCP) server that provides access to PatternFly rules and documentation, built with Node.js.
The PatternFly MCP server is a comprehensive library resource for PatternFly. It is intended to be extensible to meet the needs of different teams and projects, from simple to complex, from design to development. Read more about our roadmap and how we've structured the server in our architecture docs.
Requirements
- Node.js 20+
- NPM (or equivalent package manager)
Quick Start
The PatternFly MCP Server supports multiple configurations; see the usage documentation for details.
For integrated use with an IDE
Set a basic MCP configuration
Minimal configuration
{
"mcpServers": {
"patternfly-docs": {
"command": "npx",
"args": ["-y", "@patternfly/patternfly-mcp@latest"],
"description": "PatternFly rules and documentation"
}
}
}
HTTP transport mode
{
"mcpServers": {
"patternfly-docs": {
"command": "npx",
"args": ["-y", "@patternfly/patternfly-mcp@latest", "--http", "--port", "8080"],
"description": "PatternFly docs (HTTP transport)"
}
}
}
See the MCP Server Configuration documentation for more examples.
For development, advanced usage
Run the server directly
Run the server immediately via npx:
npx -y @patternfly/patternfly-mcp
Or with options
npx -y @patternfly/patternfly-mcp --log-stderr --verbose
Inspect the server
Visualize and test the MCP interface:
npx -y @modelcontextprotocol/inspector npx @patternfly/patternfly-mcp
Embed the server in your application
import { start } from '@patternfly/patternfly-mcp';
// Remember to avoid using console.log and info, they pollute STDOUT
async function main() {
const server = await start();
// Graceful shutdown
process.on('SIGINT', async () => {
await server.stop();
process.exit(0);
});
}
main();
See the development documentation for additional examples, CLI and embedded server options.
Documentation
For comprehensive usage, development, and project state read the docs.
Contributing
Contributing? Guidelines can be found here CONTRIBUTING.md.
AI agent
If you're using an AI assistant to help with development in this repository, please prompt it to review the repo guidelines to ensure adherence to project conventions.
Guidelines for developer-agent interaction can be found in CONTRIBUTING.md.