MCP Prompt Optimizer
An MCP server that automatically analyzes and optimizes AI prompts using the OTA (Optimize-Then-Answer) Framework
🎯 What It Does
This MCP server provides an optimize_prompt tool that:
- 📊 Analyzes prompts - Calculates clarity score (0-100%) and identifies domain
- 🔍 Detects risks - Flags security, privacy, policy, safety, and compliance concerns
- ❓ Asks smart questions - Generates 1-3 targeted questions when clarity < 60%
- ✨ Enhances prompts - Adds domain-specific requirements (tests for code, accessibility for UX, etc.)
- 📋 Provides structure - Returns optimized prompts ready for AI processing
🚀 Quick Start
Installation
For Claude Code:
# Clone the repository
git clone https://github.com/grandinh/mcp-prompt-optimizer.git
cd mcp-prompt-optimizer
# Install dependencies
npm install
# Build
npm run build
Add to .mcp.json:
{
"mcpServers": {
"prompt-optimizer": {
"command": "node",
"args": ["/path/to/mcp-prompt-optimizer/dist/index.js"],
"description": "Optimizes prompts using the OTA Framework"
}
}
}
Restart your MCP client (Claude Code, Cursor, etc.)
Usage
Option 1: Use the MCP tool directly
Once installed, use the optimize_prompt tool:
Use the optimize_prompt tool to analyze: "build a dashboard"
Option 2: Use the /ori slash command (Claude Code)
The /ori (Optimize-Research-Implement) command provides an autonomous workflow with intelligent multi-model selection:
/ori add JWT authentication to the Express API
This will: 0. Strategy (Opus) - Design optimal research plan and select best models
- Research (Dynamic) - Automatically search docs, best practices, and codebase
- Verify (Sonnet) - Cross-validate findings and check for risks
- Implement (Sonnet/Haiku) - Apply changes with error handling
- Document (Haiku) - Update README, CHANGELOG, and other docs
Multi-Model Benefits:
- 40% cost reduction vs. all-Opus
- 30% faster execution
- Each model used in its optimal zone
See /ori command documentation for details.
Output:
[OPTIMIZED] Domain: code | Clarity: 30% | Risks: none
⚠️ Clarification Needed (Clarity: 30%)
Please answer these questions before I proceed:
1. What programming language or framework are you using?
2. What specific features or components are you building?
3. Do you need tests, validation, or specific security considerations?
After answering:
Use optimize_prompt tool: "build a React dashboard with user analytics,
chart visualizations using Chart.js, and real-time data updates.
Need responsive design and accessibility compliance."
Output:
[OPTIMIZED] Domain: code | Clarity: 85% | Risks: none
✓ Ready to Process (Clarity: 85%)
[Shows enhanced prompt with code-specific requirements including
security, testing, accessibility, and structured output format]
📊 Features
Domain Detection
Automatically identifies the domain of your request:
- code - Programming, APIs, debugging
- UX - UI design, interfaces, accessibility
- data - Analytics, statistics, calculations
- writing - Content, documentation, articles
- research - Studies, investigations, analysis
- finance - ROI, budgets, pricing
- product - Features, roadmaps, strategy
Clarity Scoring
Calculates a 0-1 clarity score based on:
| Factor | Weight | Measures |
|---|---|---|
| Goal clarity | 30% | Is objective explicit and measurable? |
| Context completeness | 25% | Are inputs/constraints provided? |
| Format specification | 15% | Is output format defined? |
| Success criteria | 20% | Are acceptance criteria stated? |
| Technical detail | 10% | Stack, versions, specifics included? |
Risk Detection
Flags potential concerns:
- security - auth, passwords, tokens, vulnerabilities
- privacy - PII, email, phone, GDPR
- policy - fake, bypass, illegal activities
- safety - harm, dangerous content
- compliance - medical/legal/financial advice
Smart Questions
When clarity < 60%, generates targeted questions:
Code domain:
- What programming language or framework?
- What specific feature/component?
- Testing/security needs?
UX domain:
- Who are the target users?
- What platform (web/mobile)?
Data domain:
- What's the data structure?
- What specific metrics?
Domain-Specific Enhancement
Adds requirements based on domain:
Code:
- Include code summary and complexity notes
- Add security considerations
- Provide test plan and example I/O
- Include error handling
UX:
- Evaluate against usability heuristics
- Include accessibility checklist (WCAG 2.1 AA)
- Consider mobile responsiveness
- Address error and loading states
Data:
- Describe dataset shape and structure
- Show calculation steps explicitly
- Validate data and identify edge cases
- Make results reproducible
📖 Examples
Example 1: Vague Request
Input:
optimize_prompt: "help me with my app"
Output:
[OPTIMIZED] Domain: code | Clarity: 25% | Risks: none
⚠️ Clarification Needed
1. What programming language or framework are you using?
2. What specific feature or component are you building?
Example 2: Clear Request with Security
Input:
optimize_prompt: "Create a REST API for user authentication in Express.js.
Need endpoints for register, login, logout. Use JWT tokens with refresh
token rotation. Store passwords with bcrypt."
Output:
[OPTIMIZED] Domain: code | Clarity: 90% | Risks: security
✓ Ready to Process
**Domain:** code
**Requirements:**
- Include code summary and complexity notes
- **CRITICAL:** Address security concerns (auth, validation, token storage)
- Provide test plan and example I/O
- Include error handling
**Risk Flags:** security
Please address these concerns in your response.
Example 3: UX Request
Input:
optimize_prompt: "Review my checkout flow for usability issues"
Output:
[OPTIMIZED] Domain: UX | Clarity: 70% | Risks: none
✓ Ready to Process
**Requirements:**
- Evaluate against usability heuristics
- Include accessibility checklist (WCAG 2.1 AA)
- Consider mobile responsiveness
- Address error and loading states
🔧 Configuration
Adjust Clarity Threshold
Edit src/index.ts:
const needsClarification = clarityScore < 0.6; // Change to 0.7 for stricter
Change Question Limit
In generateQuestions():
return questions.slice(0, 3); // Change to 2 for fewer questions
Add Custom Domain
Add to detectDomain():
if (/(your|custom|keywords)/i.test(prompt)) {
return 'your_domain';
}
Then add handling in generateQuestions() and createOptimizedPrompt().
🏗️ Development
Build
npm run build
Watch Mode
npm run dev
Project Structure
mcp-prompt-optimizer/
├── src/
│ └── index.ts # Main server code
├── dist/ # Built output (git-ignored)
├── package.json
├── tsconfig.json
├── README.md
├── LICENSE
└── .gitignore
🎓 How It Works
The OTA (Optimize-Then-Answer) Loop
1. Parse & Classify
├── Detect domain
├── Calculate clarity score
└── Identify risk flags
2. Generate Questions (if clarity < 60%)
└── Max 3 targeted questions
3. Create Optimized Prompt
├── Add domain-specific requirements
├── Include risk warnings
└── Specify output format
4. Return Analysis
├── Optimization header
├── Questions (if needed)
└── Enhanced prompt (if ready)
Keyword-Based Detection
The server uses keyword matching for:
- Domain classification - Fast, deterministic
- Clarity scoring - Heuristic-based
- Risk detection - Pattern matching
Note: This is intentionally simple and fast. No ML models, no API calls, works offline.
🤝 Contributing
Contributions welcome! Areas for improvement:
- ML-based domain classification
- Multi-language support
- Learning from user feedback
- Integration with custom knowledge bases
- Automatic prompt rewriting (not just enhancement)
📄 License
MIT License - see LICENSE file for details
🔗 Related
⭐ Support
If this tool helps you get better AI responses, give it a star!
📝 Changelog
v1.1.0 (2025-11-08)
- Added
/orislash command for autonomous research-implement workflow - Intelligent multi-model selection (Opus → Sonnet → Haiku)
- Phase 0: Opus creates research strategy
- Phase 1: Dynamic model selection based on complexity
- Phase 2-4: Optimized model per phase (40% cost savings)
- Integrated OODA framework with OTA Loop in optimized_prompts.md
- Added automatic web search and documentation research
- Implemented error handling and rollback mechanisms
- Added automatic documentation updates (README, CHANGELOG)
- Created configurable workflow via
.claude/ori-config.json
v1.0.0 (2025-11-08)
- Initial release
- Domain detection (7 domains)
- Clarity scoring (0-1 scale)
- Risk detection (5 categories)
- Smart question generation (max 3)
- Domain-specific prompt enhancement
Made with ❤️ for better AI interactions