Mentioned in template examples for specialized code review and analysis, enabling Django framework-specific prompt templates
Used for repository cloning during installation, allowing users to access and deploy the MCP server from the GitHub repository
Supported as a platform for running the MCP server with specific configuration file locations for Claude Desktop integration
Used for dependency management and running server tests, enabling proper installation and verification of the MCP server components
Referenced in template examples for data analysis and code review, allowing the creation of Python-specific prompt templates
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 scoring
- 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 the repository:
- Install dependencies:
- Test the server:
- The server is now ready to be configured in Claude Desktop
Configuration
Add to your Claude Desktop config:
macOS
~/Library/Application Support/Claude/claude_desktop_config.json
Windows
%APPDATA%\Claude\claude_desktop_config.json
Usage Examples
Creating a Template
Executing a Template
Tracking Performance
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
Data Analysis
Quality Scoring
Templates are scored 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" score 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 scores?
- 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? Feel free to contribute!
License
MIT License - feel free to use and modify as needed.
This server cannot be installed
A Model Context Protocol server that helps users create, validate, and optimize AI prompts using the RISEN framework (Role, Instructions, Steps, Expectations, Narrowing).
Related MCP Servers
- -securityAlicense-qualityA TypeScript implementation of a Model Context Protocol server that provides a frictionless framework for developers to build and deploy AI tools and prompts, focusing on developer experience with zero boilerplate and automatic tool registration.Last updated -6TypeScriptMIT License
- -securityFlicense-qualityA Model Context Protocol server that enables role-based context management for AI agents, allowing users to establish specific instructions, maintain partitioned memory, and adapt tone for different agent roles in their system.Last updated -TypeScript
- -securityFlicense-qualityA Model Context Protocol implementation for managing and serving AI prompts with a TypeScript-based architecture in a monorepo structure.Last updated -28,526,5832TypeScript
- AsecurityFlicenseAqualityA Model Context Protocol server that enables AI agents to generate, fetch, and manage UI components through natural language interactions.Last updated -3194TypeScript