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MCP Prompt Optimizer

by grandinh
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JavaScript
Remote

MCP Prompt Optimizer

An MCP server that automatically analyzes and optimizes AI prompts using the OTA (Optimize-Then-Answer) Framework

License: MIT TypeScript MCP

🎯 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

{ "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

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

  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 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 /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

One-click Deploy
A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

How are these scores calculated?

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