by patternfly
Access PatternFly documentation and React docs, component schemas, and dev rules via PatternFly MCP Server — AI-assisted
Provides PatternFly React component documentation, schemas, and development best practices to AI assistants. Helps developers build applications using PatternFly components correctly.
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.
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.
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.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
According to our notes, PatternFly MCP Server benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
PatternFly MCP Server is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
PatternFly MCP Server has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
According to our notes, PatternFly MCP Server benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
PatternFly MCP Server has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
PatternFly MCP Server reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
I recommend PatternFly MCP Server for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
PatternFly MCP Server reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We wired PatternFly MCP Server into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
We evaluated PatternFly MCP Server against two servers with overlapping tools; this profile had the clearer scope statement.
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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.
The PatternFly MCP Server supports multiple configurations; see the usage documentation for details.
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.
Run the server immediately via npx:
npx -y @patternfly/patternfly-mcp
Or with options
npx -y @patternfly/patternfly-mcp --log-stderr --verbose
Visualize and test the MCP interface:
npx -y @modelcontextprotocol/inspector npx @patternfly/patternfly-mcp
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.
For comprehensive usage, development, and project state read the docs.
Contributing? Guidelines can be found here CONTRIBUTING.md.
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.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.