
RISEN Prompt Engineering MCP Tool
A Model Context Protocol server that helps users create, validate, manage, and optimize prompts using the RISEN framework (Role, Instructions, Steps, Expectations, Narrowing).
README
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:
- Role: Define the AI's persona/expertise
- Instructions: Clear directives for the task
- Steps: Breakdown of the process
- Expectations: Desired outcome/format
- Narrowing: 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
- Clone or download this repository
- Install dependencies:
npm install
- Test the server:
npm test
- The server is now ready to be configured in Claude Desktop
Configuration
Add to your Claude Desktop config file:
Windows
{
"mcpServers": {
"risen-prompts": {
"command": "node",
"args": ["/path/to/mcp-risen-prompts/server.js"],
"cwd": "/path/to/mcp-risen-prompts"
}
}
}
macOS/Linux
{
"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
- risen_create - Create new RISEN templates
- risen_validate - Check structure and get suggestions
- risen_execute - Run templates with variables
- risen_track - Record performance metrics
- risen_search - Find templates by tags/rating
- risen_analyze - Get insights on template performance
- risen_suggest - AI-powered improvement recommendations
- 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
- Be Specific: Vague roles like "assistant" rate lower than "Senior Python developer with AWS expertise"
- Use Variables: Make templates reusable with
{{variables}}
- Measurable Expectations: Include numbers (word count, examples needed, etc.)
- Clear Steps: Each step should be actionable and specific
- 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:
- Create/select a RISEN template
- Use Cross-AI to run it on ChatGPT, Gemini, and Claude
- Compare results and track which model performs best
With Knowledge Base
Save successful prompts for future reference:
- Create and test a RISEN prompt
- Once proven effective, save to Knowledge Base
- 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.
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