MCP Prompt Optimizer

# MCP Prompt Optimizer > An MCP server that automatically analyzes and optimizes AI prompts using the OTA (Optimize-Then-Answer) Framework [](https://opensource.org/licenses/MIT) [](https://www.typescriptlang.org/) [](https://modelcontextprotocol.io/) ## 🎯 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:** ```bash # 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`:** ```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: ```bash /ori add JWT authentication to the Express API ``` This will: 0. **Strategy (Opus)** - Design optimal research plan and select best models 1. **Research (Dynamic)** - Automatically search docs, best practices, and codebase 2. **Verify (Sonnet)** - Cross-validate findings and check for risks 3. **Implement (Sonnet/Haiku)** - Apply changes with error handling 4. **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](.claude/commands/ori.md) 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`: ```typescript const needsClarification = clarityScore < 0.6; // Change to 0.7 for stricter ``` ### Change Question Limit In `generateQuestions()`: ```typescript return questions.slice(0, 3); // Change to 2 for fewer questions ``` ### Add Custom Domain Add to `detectDomain()`: ```typescript if (/(your|custom|keywords)/i.test(prompt)) { return 'your_domain'; } ``` Then add handling in `generateQuestions()` and `createOptimizedPrompt()`. ## 🏗️ Development ### Build ```bash npm run build ``` ### Watch Mode ```bash 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](LICENSE) file for details ## 🔗 Related - [Model Context Protocol](https://modelcontextprotocol.io/) - [Optimized Prompts Framework Documentation](https://github.com/grandinh/mcp-prompt-optimizer/blob/main/docs/framework.md) *(coming soon)* ## ⭐ Support If this tool helps you get better AI responses, give it a star! ## 📝 Changelog ### v1.1.0 (2025-11-08) - Added `/ori` slash 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**