RISEN Prompt Engineering MCP Tool

# RISEN Prompt Engineering MCP Tool A powerful Model Context Protocol (MCP) server that helps you create, validate, manage, and optimize prompts using the RISEN framework. ## What is RISEN? RISEN is a structured prompt engineering framework with 5 components: - **R**ole: Define the AI's persona/expertise - **I**nstructions: Clear directives for the task - **S**teps: Breakdown of the process - **E**xpectations: Desired outcome/format - **N**arrowing: Constraints or creative elements ## Features ### 🎯 Core Functionality - **Template Management**: Create, store, and organize RISEN prompt templates - **Variable Support**: Use `{{variables}}` for dynamic, reusable prompts - **Validation Engine**: Real-time structure checking and quality rating - **Performance Tracking**: Monitor prompt effectiveness with ratings and analytics - **AI Suggestions**: Get improvement recommendations based on best practices ### 🚀 Advanced Features - **A/B Testing**: Compare different prompt variations - **Cross-AI Integration**: Works with your Cross-AI tool to test prompts on multiple models - **Knowledge Base Integration**: Save successful prompts for future reference - **Natural Language Conversion**: Transform regular requests into RISEN format - **Template Library**: Pre-built templates for common tasks ## Installation 1. **Clone or download** this repository 2. **Install dependencies**: ```bash npm install ``` 3. **Test the server**: ```bash npm test ``` 4. The server is now ready to be configured in Claude Desktop ## Configuration Add to your Claude Desktop config file: ### Windows ```json { "mcpServers": { "risen-prompts": { "command": "node", "args": ["/path/to/mcp-risen-prompts/server.js"], "cwd": "/path/to/mcp-risen-prompts" } } } ``` ### macOS/Linux ```json { "mcpServers": { "risen-prompts": { "command": "node", "args": ["/path/to/mcp-risen-prompts/server.js"], "cwd": "/path/to/mcp-risen-prompts" } } } ``` **Replace** `/path/to/mcp-risen-prompts` with your actual installation path. ## Usage Examples ### Creating a Template ``` Use risen_create to make a new template: - Name: "Code Review" - Role: "Senior software engineer with 15+ years experience" - Instructions: "Review the provided code for quality and security" - Steps: ["Analyze structure", "Check for bugs", "Suggest improvements"] - Expectations: "Detailed line-by-line feedback with examples" - Narrowing: "Focus on critical issues first" ``` ### Executing a Template ``` Use risen_execute with variables: - Template ID: [your-template-id] - Variables: {"language": "Python", "framework": "Django"} ``` ### Tracking Performance ``` After using a prompt, track its effectiveness: - Use risen_track - Rate 1-5 stars - Add notes about what worked/didn't work ``` ## MCP Tools Available 1. **risen_create** - Create new RISEN templates 2. **risen_validate** - Check structure and get suggestions 3. **risen_execute** - Run templates with variables 4. **risen_track** - Record performance metrics 5. **risen_search** - Find templates by tags/rating 6. **risen_analyze** - Get insights on template performance 7. **risen_suggest** - AI-powered improvement recommendations 8. **risen_convert** - Transform natural language to RISEN ## Template Examples ### Blog Post Writer ``` Role: Content strategist and SEO expert Instructions: Write an engaging blog post about {{topic}} Steps: 1. Research keywords and trends 2. Create compelling headline 3. Develop main points with examples 4. Include statistics and sources 5. Write conclusion with CTA Expectations: 1500-2000 words, SEO-optimized, engaging tone Narrowing: Use conversational tone, include 3-5 keywords naturally ``` ### Data Analysis ``` Role: Data scientist specializing in {{domain}} Instructions: Analyze {{dataset}} to uncover insights Steps: 1. Perform exploratory data analysis 2. Identify key trends and patterns 3. Run statistical tests 4. Create visualizations 5. Provide recommendations Expectations: Clear insights with statistical backing Narrowing: Focus on {{specific_metrics}} and business impact ``` ## Quality Rating Templates are rated out of 100 based on: - Role specificity (20 points) - Instruction clarity (20 points) - Step detail (20 points) - Expectation metrics (20 points) - Narrowing focus (20 points) ## Best Practices 1. **Be Specific**: Vague roles like "assistant" rate lower than "Senior Python developer with AWS expertise" 2. **Use Variables**: Make templates reusable with `{{variables}}` 3. **Measurable Expectations**: Include numbers (word count, examples needed, etc.) 4. **Clear Steps**: Each step should be actionable and specific 5. **Test & Iterate**: Use tracking to refine templates over time ## Integration with Other MCP Tools ### With Cross-AI Tool Execute the same RISEN prompt across multiple AI models: 1. Create/select a RISEN template 2. Use Cross-AI to run it on ChatGPT, Gemini, and Claude 3. Compare results and track which model performs best ### With Knowledge Base Save successful prompts for future reference: 1. Create and test a RISEN prompt 2. Once proven effective, save to Knowledge Base 3. Search and retrieve proven prompts by topic ## Troubleshooting **Template not validating?** - Ensure all required fields are filled - Check that steps is an array, not a string - Verify variables are properly declared **Variables not replacing?** - Use exact syntax: `{{variable_name}}` - Ensure variable names match in declaration and usage - Check that all variables have values when executing **Low quality ratings?** - Add more detail to each component - Include specific metrics in expectations - Use domain-specific language in role ## Future Roadmap - [ ] Visual template builder UI - [ ] Community template marketplace - [ ] Advanced analytics dashboard - [ ] Prompt chaining workflows - [ ] Export/import template packs - [ ] Team collaboration features ## Contributing Found a bug or have a feature request? Contributions are welcome! ## License MIT License - feel free to use and modify as needed.