📝 System Prompts MCP Server

Access system prompts from AI tools in your workflow. Browse and fetch prompts from Devin, Cursor, Claude, GPT, and more. Model-aware suggestions help you find the perfect prompt for your LLM.

An MCP (Model Context Protocol) server that exposes a collection of system prompts, summaries, and tool definitions from popular AI tools as MCP tools for AI coding environments like Cursor and Claude Desktop.

Why Use System Prompts MCP?

• 🔍 Automatic Discovery – Every prompt in prompts/ is automatically exposed as an MCP tool
• 🎯 Model-Aware Suggestions – Get prompt recommendations based on your LLM (Claude, GPT, Gemini, etc.)
• 📚 Comprehensive Collection – Access prompts from Devin, Cursor, Claude, GPT, and more
• 🚀 Easy Setup – One-click install in Cursor or simple manual setup
• 🔧 Extensible – Add your own prompts and they're automatically available

Quick Start

Ready to explore system prompts? Install in seconds:

Install in Cursor (Recommended):

🔗 Install in Cursor

Or install manually:

npm install -g system-prompts-mcp-server
# Or from source:
git clone https://github.com/JamesANZ/system-prompts-and-models-of-ai-tools.git
cd system-prompts-and-models-of-ai-tools && npm install && npm run build

Features

Core Tools

• list_prompts – Browse available prompts with filters (service, flavor, provider)
• get_prompt_suggestion – Get ranked prompt suggestions for your LLM and keywords
• -- – Direct access to any prompt (e.g., cursor-agent-system, devin-summary)

Automatic Discovery

• Scans prompts/ directory for .txt, .md, .yaml, .yml, .json files
• Each file becomes a dedicated MCP tool
• Infers metadata (service, variant, LLM family, persona hints)

Persona Activation

• Each tool call includes a reminder for the model to embody the loaded prompt
• Helps models behave like the original service (Devin, Cursor, etc.)

Installation

Cursor (One-Click)

Click the install link above or use:

cursor://anysphere.cursor-deeplink/mcp/install?name=system-prompts-mcp&config=eyJzeXN0ZW0tcHJvbXB0cy1tY3AiOnsiY29tbWFuZCI6Im5weCIsImFyZ3MiOlsiLXkiLCJzeXN0ZW0tcHJvbXB0cy1tY3Atc2VydmVyIl19fQ==

Manual Installation

Requirements: Node.js 18+ and npm

# Clone and build
git clone https://github.com/JamesANZ/system-prompts-and-models-of-ai-tools.git
cd system-prompts-and-models-of-ai-tools
npm install
npm run build

# Run server
npm start

Claude Desktop

Add to claude_desktop_config.json:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "system-prompts-mcp": {
      "command": "node",
      "args": ["/absolute/path/to/system-prompts-and-models-of-ai-tools/dist/index.js"],
      "env": {
        "PROMPT_LIBRARY_ROOT": "/absolute/path/to/system-prompts-and-models-of-ai-tools/prompts"
      }
    }
  }
}

Restart Claude Desktop after configuration.

Usage Examples

List Available Prompts

Browse prompts with optional filters:

{
  "tool": "list_prompts",
  "arguments": {
    "service": "cursor",
    "flavor": "system",
    "limit": 10
  }
}

Get Prompt Suggestions

Find the best prompt for your LLM and use case:

{
  "tool": "get_prompt_suggestion",
  "arguments": {
    "userLlm": "claude-3.5-sonnet",
    "keywords": ["code", "pair programming"]
  }
}

Access a Specific Prompt

Call a prompt directly by its tool name:

{
  "tool": "cursor-agent-system",
  "arguments": {}
}

Get structured metadata only:

{
  "tool": "cursor-agent-system",
  "arguments": {
    "format": "json"
  }
}

Adding Your Own Prompts

Add prompts by placing files in the prompts/ directory:

Supported formats: .txt, .md, .yaml, .yml, .json

Directory structure:

prompts/My Service/
  ├── System Prompt.txt     → Tool: "my-service-system-prompt-system"
  └── tools.json            → Tool: "my-service-tools-tools"

• Directory names become the service name
• File names create tool variants
• Files are automatically classified as system prompts, tools, or summaries

After adding prompts, restart the MCP server. Use list_prompts to find your custom prompts.

Custom directory: Set PROMPT_LIBRARY_ROOT environment variable to use a different location.

Use Cases

• AI Tool Developers – Reference and adapt prompts from successful AI tools
• Researchers – Study how different tools structure their system prompts
• Developers – Find the perfect prompt for your LLM and use case
• Prompt Engineers – Compare and learn from proven prompt patterns

Technical Details

Built with: Node.js, TypeScript, MCP SDK
Dependencies: @modelcontextprotocol/sdk, zod
Platforms: macOS, Windows, Linux

Environment Variables:

• PROMPT_LIBRARY_ROOT (optional): Override prompt root directory (defaults to prompts/)

Project Structure:

• src/ – TypeScript MCP server implementation
• dist/ – Compiled JavaScript
• prompts/ – Prompt library and original documentation

Contributing

⭐ If this project helps you, please star it on GitHub! ⭐

Contributions welcome! Feel free to adapt the discovery logic, add tests, or extend metadata inference for new prompt formats.

License

See the original repository for license information.

Support

If you find this project useful, consider supporting it:

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