hariombangari/mcp-server-prompt-library

Prompt Library MCP Server

A Model Context Protocol (MCP) server that provides a comprehensive prompt library for frontend developers. This server specializes in React, frontend best practices, and general coding guidelines, designed to enhance developer productivity through AI-powered assistance.

Features

• Categorized Prompt Library: Organized prompts for React, Frontend (FE), and Common development practices
• Multiple Retrieval Methods: Get prompts by category, search by keywords, or combine multiple prompts
• Cloudflare Workers Deployment: Serverless architecture for global edge distribution
• MCP Compatible: Seamless integration with MCP clients and IDEs
• Clean Architecture: Structured for easy maintenance and extensibility

Available Categories

React

• Component Creation: Best practices for React functional components with TypeScript
• State Management: Guidelines for useState, useReducer, and context management

Frontend (FE)

• Performance Optimization: Core Web Vitals, loading strategies, and runtime performance
• Accessibility: WCAG 2.1 guidelines and a11y best practices

Common

• Code Quality: Clean code principles, SOLID principles, and testing strategies

MCP Tools

get_categories

Returns all available prompt categories in the library.

Parameters: None

get_prompts_by_category

Retrieves all prompts from a specific category.

Parameters:

• category (enum): "react" | "fe" | "common"

get_prompt

Retrieves a specific prompt by name and category.

Parameters:

• category (enum): "react" | "fe" | "common"
• name (string): Name of the prompt

combine_prompts

Combines prompts from multiple categories into a single response.

Parameters:

• categories (array): Array of category names
• separator (string, optional): Custom separator between prompts (default: "\n\n---\n\n")

search_prompts

Searches prompts by keyword across all or specific categories.

Parameters:

• keyword (string): Search term
• categories (array, optional): Limit search to specific categories

Getting Started

Prerequisites

• Node.js 18+
• A Cloudflare account with Workers access
• pnpm (recommended package manager)

Installation

1. Clone this repository
2. Install dependencies:

   pnpm install
   

Development

Run the development server:

pnpm run dev

This will start the Wrangler development server locally.

Deployment

Deploy to Cloudflare Workers:

pnpm run deploy

Usage

Once deployed, connect to your MCP server using any MCP-compatible client:

• MCP Endpoint: https://your-worker.your-subdomain.workers.dev/mcp
• SSE Endpoint: https://your-worker.your-subdomain.workers.dev/sse

Example Usage in IDE

// Get all React prompts
await mcpClient.callTool("get_prompts_by_category", { category: "react" });

// Combine React and FE prompts for comprehensive guidance
await mcpClient.callTool("combine_prompts", { 
  categories: ["react", "fe"] 
});

// Search for performance-related prompts
await mcpClient.callTool("search_prompts", { 
  keyword: "performance" 
});

Connect to Claude Desktop

You can connect to your remote MCP server from Claude Desktop using mcp-remote proxy.

Update Claude Desktop configuration in Settings > Developer > Edit Config:

{
  "mcpServers": {
    "prompt-library": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "http://localhost:8787/sse"  // or your-worker.your-account.workers.dev/sse
      ]
    }
  }
}

Restart Claude and the prompt library tools will become available.

Connect to Cloudflare AI Playground

You can test your MCP server from the Cloudflare AI Playground:

1. Go to https://playground.ai.cloudflare.com/
2. Enter your deployed MCP server URL (your-worker.your-subdomain.workers.dev/sse)
3. Use the prompt library tools directly from the playground!

Architecture

This project uses:

• Cloudflare Workers: Serverless edge deployment for global availability
• MCP SDK: Model Context Protocol implementation for seamless integration
• TypeScript: Type safety and enhanced developer experience
• Zod: Runtime type validation for tool parameters
• Structured Prompt Management: Organized, maintainable prompt library

Adding New Prompts

To add new prompts:

1. Add the prompt content to the loadPrompts() method in src/index.ts
2. Follow the existing structure for consistency
3. Update the category enum in TypeScript types if adding new categories
4. Test locally before deploying

Future Enhancements

• Dynamic Prompt Loading: Load prompts from external sources or databases
• Gemini Integration: Enhanced prompt processing with Google's Gemini API
• Additional Categories: Backend, Python, mobile development, etc.
• Version Management: Track and manage prompt versions
• Analytics: Usage tracking and prompt effectiveness metrics

Contributing

1. Fork the repository
2. Create a feature branch
3. Add or modify prompts following the established patterns
4. Test locally with pnpm run dev
5. Deploy and test on Cloudflare Workers
6. Submit a pull request

License

This project is private and proprietary. All rights reserved.