MCP Prompt Optimizer v3.0.3

🚀 Professional cloud-based MCP server for AI-powered prompt optimization with intelligent context detection, template management, team collaboration, and enterprise-grade reliability. Starting at $2.99/month.

✨ Key Features

🧠 AI Context Detection — Automatically detects and optimizes for code, creative writing, image generation, communication, and more 📁 Template Management — Auto-save high-confidence optimizations, search & reuse patterns 👥 Team Collaboration — Shared quotas, team templates, role-based access 📊 Confidence Scoring — Honest quality signal with per-tier annotations ☁️ Cloud Processing — Always up-to-date AI models via backend LLM pipeline 🔧 Resilient Fallback — Structured local optimization if the backend is unreachable 🎛️ Personal Model Choice — Use your own OpenRouter models via WebUI configuration 🔧 Universal MCP — Works with Claude Desktop, Cursor, Windsurf, Cline, VS Code, Zed, Replit

🚀 Quick Start

1. Get your API key (required):

• 🆓 Free Tier (sk-local-*): 5 daily optimizations — promptoptimizer-blog.vercel.app/pricing
• ⭐ Paid Tiers (sk-opt-*, sk-team-*): Higher quotas, team features, advanced capabilities

2. Install:

npm install -g mcp-prompt-optimizer

3. Configure your MCP client:

Add to ~/.claude/claude_desktop_config.json (Claude Desktop):

{
  "mcpServers": {
    "prompt-optimizer": {
      "command": "npx",
      "args": ["mcp-prompt-optimizer"],
      "env": {
        "OPTIMIZER_API_KEY": "sk-local-your-key-here"
      }
    }
  }
}

4. Restart your MCP client and start optimizing.

Note: API keys are validated against the backend server. An internet connection is required (responses are cached for up to 2 hours for reliability).

⚙️ How Optimization Works

Prompts are routed through a three-tier pipeline based on complexity and context. Each tier produces a different output format and confidence range.

Tier 1 — LLM Optimization (backend, confidence 70–95%)

The backend routes complex or high-sophistication prompts through a Gemini Flash LLM pass that genuinely rewrites and enriches the prompt. This is the highest-quality output.

# 🎯 Optimized Prompt

My Python script crashes with a KeyError on line 47. I need help diagnosing
the root cause. I am using Python 3.11 with pandas 2.0. The error occurs
when accessing dictionary keys after a merge operation.

**Confidence:** 82.0%
**AI Context:** code_generation

Tier 2 — Backend Rules Optimization (backend, confidence < 25%)

For simpler or lower-sophistication prompts the backend applies rules-based optimization without an LLM. When this happens, confidence will be below 25% and a note will appear explaining what to check if you expected full LLM enhancement.

# 🎯 Optimized Prompt

My Python script crashes with a KeyError. Please provide the error message
and relevant code.

> ℹ️ *Low confidence indicates the backend applied rules-based optimization
> (no LLM). Ensure `OPENROUTER_API_KEY` is configured in the backend for
> full LLM enhancement.*

**Confidence:** 18.0%
**AI Context:** code_generation

Tier 3 — Local Rules Fallback (npm, confidence 35–55%)

If the backend is unreachable, the npm package applies optimization locally using a library of domain-specific templates. The output is a structured, user-facing prose prompt — no XML scaffolding. The confidence annotation makes the quality tier explicit.

# 🔧 Rules-Based Optimization Applied

⚠️ *Backend unreachable — your prompt has been structured using local rule
templates (no LLM). Re-run when the backend is available for full LLM
optimization.*

**Template:** `debugging_request`

**Optimized Prompt:**

My Python script crashes with a KeyError

To address this effectively:

• Identify the most likely root causes given the error description.
• Request the full error message and stack trace, plus the relevant code snippet.
• Ask about expected vs. actual behavior, programming language, and library versions.

Response format: Step-by-step debugging walkthrough with a specific fix or next diagnostic steps.

**Confidence:** 51.0% *(rules-based — LLM optimization typically 70–95%)*
**AI Context:** code_generation

🧠 AI Context Detection

The server automatically detects your prompt type and routes it to the appropriate optimization template. You can also specify ai_context manually in the tool call.

| Context | ai_context value | Example patterns | |---|---|---| | Code & debugging | code_generation | debug, fix, error, python, sql, refactor | | Web development | code_generation | landing page, react, css, html, tailwind | | API & integration | api_automation | api, rest, endpoint, oauth, webhook | | DevOps & automation | technical_automation | deploy, docker, ci/cd, terraform, bash | | Data & schemas | structured_output | json, schema, csv, yaml, transform | | Analysis & research | llm_interaction | analyze, explain, compare, research | | Creative writing | creative_writing | story, poem, script, blog, copywriting | | Email & communication | human_communication | email, letter, memo, formal, reply | | Image generation | image_generation | photorealistic, midjourney, dall-e, portrait | | General | general_assistant | Everything else |

Image Generation

Image prompts are handled separately. The local fallback appends style-matched quality boosters directly to the prompt (comma-separated), rather than producing structured bullets. This matches how image generation models consume prompts.

Input:  "Draw a photorealistic portrait of an astronaut"
Output: "Draw a photorealistic portrait of an astronaut, ultra realistic,
         sharp focus, professional photography, dynamic lighting, balanced
         composition, high quality, 4K"

🛠️ Available MCP Tools

optimize_prompt

AI optimization with context detection, auto-save, and insights.

{
  "prompt": "Your prompt text",
  "goals": ["clarity", "specificity"],
  "ai_context": "code_generation",
  "enable_bayesian": true
}

detect_ai_context

Detect the AI context for a given prompt.

{
  "prompt": "The prompt text for which to detect the AI context"
}

create_template

Save an optimization as a reusable template.

{
  "title": "Template title",
  "description": "Optional description",
  "original_prompt": "The original prompt",
  "optimized_prompt": "The optimized prompt",
  "optimization_goals": ["clarity"],
  "confidence_score": 0.9,
  "ai_context_detected": "code_generation",
  "is_public": false,
  "tags": ["debugging", "python"]
}

get_template

Retrieve a saved template by ID.

{
  "template_id": "the-template-id"
}

update_template

Update an existing template.

{
  "template_id": "the-template-id",
  "title": "Updated title",
  "is_public": true
}

search_templates

Search your saved template library.

{
  "query": "debugging",
  "ai_context": "code_generation",
  "limit": 5,
  "sort_by": "confidence_score",
  "sort_order": "desc"
}

get_quota_status

Check subscription status and quota usage. No parameters required.

get_optimization_insights (conditional)

Bayesian optimization insights and parameter tuning recommendations. Requires the feature to be enabled in the backend; returns mock data otherwise.

{
  "analysis_depth": "detailed",
  "include_recommendations": true
}

get_real_time_status (conditional)

Real-time optimization status and AG-UI capabilities. Requires the feature to be enabled in the backend; returns mock data otherwise. No parameters required.

🎛️ Advanced Model Configuration (Optional)

Configure custom models in the WebUI and the MCP server uses them automatically.

Step 1 — Configure in WebUI:

1. Visit Dashboard
2. Go to Settings → User Settings
3. Add your OpenRouter API key (from openrouter.ai)
4. Select your preferred models for optimization and evaluation

Step 2 — Use the npm package as normal. Your WebUI model settings are applied automatically — no changes to the MCP configuration required.

Model Selection Priority

1. Your WebUI-configured models  (highest priority)
2. Request-specific model override
3. System default (google/gemini-flash-1.5-8b)

Example Model Recommendations

| Use case | Optimization model | Evaluation model | |---|---|---| | Creative / complex | anthropic/claude-3-5-sonnet | google/gemini-pro-1.5 | | Fast / simple | openai/gpt-4o-mini | openai/gpt-3.5-turbo | | Code / technical | anthropic/claude-3-5-sonnet | anthropic/claude-3-haiku |

Two different API keys:

• Service key (sk-opt-*) — your MCP Prompt Optimizer subscription
• OpenRouter key — your personal OpenRouter account for model usage costs

💰 Subscription Plans

| Plan | Price | Optimizations/month | Team members | |---|---|---|---| | 🎯 Explorer | $2.99/mo | 5,000 | 1 | | 🎨 Creator | $25.99/mo | 18,000 | 2 + 3 keys | | 🚀 Innovator | $69.99/mo | 75,000 | 5 + 10 keys |

🆓 Free trial: 5 optimizations with full feature access.

All plans include AI context detection, template management, personal model configuration, and optimization insights.

🔧 CLI Commands

mcp-prompt-optimizer check-status   # Check API key and quota status
mcp-prompt-optimizer validate-key   # Validate API key with backend
mcp-prompt-optimizer test           # Test backend integration
mcp-prompt-optimizer diagnose       # Run comprehensive diagnostic
mcp-prompt-optimizer clear-cache    # Clear validation cache
mcp-prompt-optimizer help           # Show help and setup instructions
mcp-prompt-optimizer version        # Show version information

🏢 Team Collaboration

Team API Keys (sk-team-*)

• Shared quotas across team members
• Centralized billing and management
• Team template libraries for consistency
• Role-based access control

Individual API Keys (sk-opt-*)

• Personal quotas and billing
• Individual template libraries
• Account self-management

🔐 Security & Privacy

• Encrypted data transmission
• API key validation with secure backend authentication
• Quota enforcement with real-time usage tracking
• No prompt data retained — processed and discarded immediately
• GDPR compliant

🔧 Universal MCP Client Support

Claude Desktop

{
  "mcpServers": {
    "prompt-optimizer": {
      "command": "npx",
      "args": ["mcp-prompt-optimizer"],
      "env": { "OPTIMIZER_API_KEY": "sk-opt-your-key-here" }
    }
  }
}

Cursor IDE

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "prompt-optimizer": {
      "command": "npx",
      "args": ["mcp-prompt-optimizer"],
      "env": { "OPTIMIZER_API_KEY": "sk-opt-your-key-here" }
    }
  }
}

Other clients

Windsurf, Cline, VS Code, Zed, Replit, JetBrains IDEs, and Neovim are all supported via standard MCP server configuration.

📦 Changelog

v3.0.3

• Rules fallback output rewritten — local optimization now produces user-facing prose prompts instead of raw XML scaffolding. The output is directly usable as a prompt without modification.
• All 18 local templates reworded — template principles are now phrased as user request guidance rather than AI-assistant directives, producing more natural and actionable structured prompts.
• creative_writing context now routes correctly to the creative writing template (previously fell through to a generic fallback).
• general_assistant context now maps explicitly to the LLM interaction template.
• Backend rules-tier detection — when the backend returns confidence below 25% (indicating it ran its own rules tier without an LLM), a note appears explaining the cause and how to resolve it (OPENROUTER_API_KEY configuration).
• Confidence scale annotation — rules-based fallback confidence now shows *(rules-based — LLM optimization typically 70–95%)* so users understand where their result sits relative to full LLM optimization.

v3.0.2

• Cross-platform binary compatibility improvements
• Bayesian optimization integration
• AG-UI feature flag support

v3.0.0

• API key now required for all operations
• Development mode and offline mode removed for security
• All keys validated against backend server

📞 Support & Resources

• Documentation: https://promptoptimizer-blog.vercel.app/docs
• Dashboard & model config: https://promptoptimizer-blog.vercel.app/dashboard
• Troubleshooting: https://promptoptimizer-blog.vercel.app/docs/troubleshooting
• Community support: GitHub Discussions
• Email support: [email protected] (Creator/Innovator plans)
• Enterprise: [email protected]

Get started with 5 free optimizations at promptoptimizer-blog.vercel.app/pricing