system-prompt-composer 1.0.5

A modular system prompt composition framework for AI assistants

Prompt Composer

A modular system prompt composition framework that intelligently generates system prompts for AI assistants based on available tools, task complexity, and contextual information.

Language Support

• Python: pip install system-prompt-composer
• Node.js: npm install system-prompt-composer (native bindings - no Python required!)

Quick Start

Python

import system_prompt_composer
import json

request = {
    "user_prompt": "Help me analyze this code",
    "mcp_config": {"mcpServers": {...}},
    "session_state": {"tool_call_count": 0}
}

response = system_prompt_composer.compose_system_prompt(json.dumps(request))
result = json.loads(response)
print(result["system_prompt"])

Node.js (Native)

const { composeSystemPrompt } = require('system-prompt-composer');

const request = {
  user_prompt: "Help me analyze this code",
  mcp_config: { mcpServers: {...} },
  session_state: { tool_call_count: 0 }
};

const response = await composeSystemPrompt(request);
console.log(response.system_prompt);

📖 For detailed Node.js documentation, see node/README.md

Project Structure

prompt-composer/
├── core/              # Rust core implementation
│   ├── lib.rs         # Main library with NAPI bindings
│   ├── types.rs       # Type definitions
│   ├── composition.rs # Prompt composition logic
│   └── ...
├── python/            # Python package (PyO3 bindings)
├── node/              # Node.js package (native NAPI-RS bindings)
│   ├── package.json
│   ├── index.js       # Native bindings wrapper
│   └── index.d.ts     # TypeScript definitions
├── prompts/           # Modular prompt library
│   ├── domains/       # Domain-specific prompts
│   └── behaviors/     # Behavioral guidance prompts
└── README.md          # This file

Features

• 🧠 Intelligent prompts that adapt to available MCP tools
• 📋 Automatic task planning for complex requests
• 🎯 Context-aware guidance for different domains (programming, analysis, filesystem, etc.)
• 📊 Progress monitoring for multi-step workflows
• 🔄 Modular design with composable prompt components
• ⚡ High performance with Rust core
• 🌍 Multi-language support (Python, Node.js)

Installation

Python Package

pip install system-prompt-composer

Node.js Package (Native - No Python Required!)

npm install system-prompt-composer

The Node.js package now uses native Rust bindings via NAPI-RS, eliminating the Python dependency!

API Reference

Core Functions

composeSystemPrompt(request)

Generate an intelligent system prompt based on available tools and context.

Parameters:

• request.user_prompt (string): The user's request
• request.mcp_config (object): MCP server configuration with mcpServers
• request.session_state (object): Current session state including tool_call_count
• request.domain_hints (array, optional): Domain hints like ["programming", "analysis"]
• request.task_complexity (string, optional): "Simple", "Complex", or "Auto"

Returns:

{
  system_prompt: "Generated prompt text...",
  source: "native",
  version: "1.1.0",
  // ... additional metadata
}

getStatus()

Returns system status and configuration information including available domains and behaviors.

isAvailable()

Always returns true for native bindings.

Tools Directory Feature

The system-prompt-composer supports tool-specific instruction files to improve how LLMs use MCP tools.

How It Works

When you call composeSystemPrompt() with MCP server configurations, the system automatically looks for corresponding instruction files in prompts/tools/ and includes them in the generated system prompt.

prompts/
├── behaviors/          # General AI behaviors 
├── domains/           # Domain-specific knowledge
├── tools/             # NEW: Tool-specific instructions
│   ├── desktop-commander.md
│   ├── weather-service.md
│   └── [your-mcp-server-name].md
└── server_patterns.toml

Creating Tool Instructions

Create a markdown file named after your MCP server:

# My Custom Tool Instructions

You have access to my-custom-tool with these capabilities:
- Function 1: description and best practices
- Function 2: common usage patterns

## Best Practices
- Specific guidance for effective tool usage
- Error handling approaches
- Performance considerations

Updated API Response

Tool instructions are automatically included and tracked:

{
  system_prompt: "...",
  applied_modules: [
    "planning",
    "tool:desktop-commander",  // Tool instructions included
    "tool:weather-service"
  ],
  recognized_tools: [...],
  complexity_assessment: "simple"
}

The getStatus() function now also returns available tools:

{
  available: true,
  domains: ["analysis", "filesystem", "programming"],
  behaviors: ["planning", "progress", "reasoning"], 
  tools: ["desktop-commander", "weather-service"],  // NEW
  version: "1.0.3"
}

Benefits

• Better Tool Usage: LLMs get specific guidance for each tool
• Developer Control: Customize instructions for your MCP tools
• Automatic Integration: Just add markdown files - no code changes
• Graceful Fallback: Missing tool files are safely ignored

Architecture

Native Node.js Architecture (NEW):

Node.js → NAPI-RS → Rust Core

Python Architecture:

Python → PyO3 → Rust Core  

Key Benefits of Native Bindings:

• ✅ No Python dependency for Node.js users
• ✅ Native performance - direct Rust execution
• ✅ Simple deployment - just npm install
• ✅ Better error handling
• ✅ Cross-platform binary distribution
• ✅ Smaller bundle size

Development

Building Native Node.js Package

cd node/
npm install
npm run build        # Build release binaries
npm run build:debug  # Build debug binaries
npm test            # Run tests

Python Development

cd python/
pip install -e .

Rust Core Development

cargo build --release
cargo test
cargo build --features nodejs  # For Node.js bindings
cargo build --features python  # For Python bindings

Publishing

Node.js (npm)

cd node/
npm run build       # Build native binaries
npm publish --access public

Python (PyPI)

cd python/
pip install build twine
python -m build
twine upload dist/*

Contributing

Contributions welcome! The project uses:

• Rust for the core prompt composition engine
• NAPI-RS for Node.js native bindings
• PyO3 for Python bindings
• Modular prompts in the prompts/ directory

License

MIT License - see LICENSE file for details.