Claude Prompts MCP Server

🌟 What Makes This Special? (v1.1.0 - "Intelligent Execution")

• 🧠 Semantic Analysis Engine → Automatically detects execution types without manual configuration
• 🎯 Universal Prompt Execution → Single tool with intelligent mode detection and auto-execution
• 🛡️ Smart Quality Gates → Auto-assigned validation based on prompt complexity and type
• 🔄 Zero-Configuration Reliability → No headers or manual setup required - just works intelligently
• 📊 Learning Analytics → System improves detection accuracy through usage patterns
• 🔥 Intelligent Hot-Reload System → Update prompts instantly without restarts
• 🎨 Advanced Template Engine → Nunjucks-powered with conditionals, loops, and dynamic data
• ⚡ Multi-Phase Orchestration → Robust startup sequence with comprehensive health monitoring
• 🚀 Universal MCP Compatibility → Works flawlessly with Claude Desktop, Cursor Windsurf, and any MCP client

Transform your AI assistant experience from scattered prompts to a truly intelligent execution engine that automatically understands and optimally executes any prompt across any MCP-compatible platform.

🚀 Revolutionary Interactive Prompt Management

🎯 The Future is Here: Manage Your AI's Capabilities FROM WITHIN the AI Conversation

This isn't just another prompt server – it's a living, breathing prompt ecosystem that evolves through natural conversation with your AI assistant. Imagine being able to:

# 🗣️ Create new prompts by talking to your AI
"Hey Claude, create a new prompt called 'code_reviewer' that analyzes code for security issues"
→ Claude creates, tests, and registers the prompt instantly

# ✏️ Refine prompts through conversation
"That code reviewer prompt needs to also check for performance issues"
→ Claude modifies the prompt and hot-reloads it immediately

# 🔍 Discover and iterate on your prompt library
>>listprompts
→ Browse your growing collection, then ask: "Improve the research_assistant prompt to be more thorough"

# 🧠 Execute prompts with zero configuration - system auto-detects everything
>>content_analysis my content
→ Automatic semantic analysis detects workflow type, applies quality gates, executes perfectly

🌟 Why This Changes Everything:

• 🧠 True Intelligence: System understands prompts like a human - no configuration needed
• 🔄 Self-Evolving System: Your AI assistant literally builds and improves its own capabilities in real-time
• 🎮 Zero Friction: Never configure execution modes, quality gates, or headers - everything just works
• ⚡ Instant Perfection: Create → Auto-detect → Execute optimally in one seamless flow
• 🌱 Learning System: Detection accuracy improves through usage - gets smarter over time

This is what truly intelligent AI infrastructure looks like – where the system understands intent as naturally as reading human language.

⚡ Features & Reliability

🎯 One-Command Installation

Get your AI command center running in under a minute:

# Clone → Install → Launch → Profit! 🚀
git clone https://github.com/minipuft/claude-prompts-mcp.git
cd claude-prompts-mcp/server && npm install && npm run build && npm start

🔌 Universal MCP Client Integration

Claude Desktop

Drop this into your claude_desktop_config.json:

{
  "mcpServers": {
    "claude-prompts-mcp": {
      "command": "node",
      "args": ["E:\\path\\to\\claude-prompts-mcp\\server\\dist\\index.js"],
      "env": {
        "MCP_PROMPTS_CONFIG_PATH": "E:\\path\\to\\claude-prompts-mcp\\server\\promptsConfig.json"
      }
    }
  }
}

Cursor Windsurf & Other MCP Clients

Configure your MCP client to connect via STDIO transport:

• Command: node
• Args: ["path/to/claude-prompts-mcp/server/dist/index.js"]
• Environment: MCP_PROMPTS_CONFIG_PATH=path/to/promptsConfig.json

💡 Pro Tip: Use absolute paths for bulletproof integration across all MCP clients!

🎮 Start Building Immediately (v1.1.0 Enhanced)

Your AI command arsenal is ready with enhanced reliability:

# 🧠 Discover your intelligent superpowers
>>listprompts

# 🎯 Zero-config intelligent execution - system auto-detects everything
>>friendly_greeting name="Developer"
→ Auto-detected as template, returns personalized greeting

>>content_analysis my research data
→ Auto-detected as workflow, applies quality gates, executes analysis framework

>>notes my content
→ Auto-detected as chain, validates each step, executes sequence

# 📊 Monitor intelligent detection performance
>>execution_analytics {"include_history": true}
→ See how accurately the system detects prompt types and applies gates

# 🚀 Create prompts that just work (zero configuration)
"Create a prompt called 'bug_analyzer' that finds and explains code issues"
→ AI creates prompt, system auto-detects workflow type, assigns quality gates

# 🔄 Refine prompts through conversation (intelligence improves)
"Make the bug_analyzer prompt also suggest performance improvements"
→ Prompt updated, system re-analyzes, updates detection profile automatically

# 🧠 Build intelligent AI workflows
"Create a prompt chain that reviews code, validates output, tests it, then documents it"
→ Chain created, each step auto-analyzed, appropriate gates assigned automatically

# 🎛️ Manual override when needed (but rarely necessary)
>>execute_prompt {"command": ">>content_analysis data", "step_confirmation": true}
→ Force step confirmation for sensitive analysis

🌟 The Magic: Your prompt library becomes a living extension of your workflow, growing and adapting as you work with your AI assistant.

🔥 Why Developers Choose This Server

# Edit any prompt file → Server detects → Reloads automatically → Zero downtime

Analyze {{content}} for {% if focus_area %}{{focus_area}}{% else %}general{% endif %} insights.

{% for requirement in requirements %}
- Consider: {{requirement}}
{% endfor %}

{% if previous_context %}
Build upon: {{previous_context}}
{% endif %}

Phase 1: Foundation → Config, logging, core services
Phase 2: Data Loading → Prompts, categories, validation
Phase 3: Module Init → Tools, executors, managers
Phase 4: Server Launch → Transport, API, diagnostics

{
  "id": "content_analysis_chain",
  "name": "Content Analysis Chain",
  "isChain": true,
  "chainSteps": [
    {
      "stepName": "Extract Key Points",
      "promptId": "extract_key_points",
      "inputMapping": { "content": "original_content" },
      "outputMapping": { "key_points": "extracted_points" }
    },
    {
      "stepName": "Analyze Sentiment",
      "promptId": "sentiment_analysis",
      "inputMapping": { "text": "extracted_points" },
      "outputMapping": { "sentiment": "analysis_result" }
    }
  ]
}

📊 System Architecture

graph TB
    A[Claude Desktop] -->|MCP Protocol| B[Transport Layer]
    B --> C[🧠 Orchestration Engine]
    C --> D[📝 Prompt Manager]
    C --> E[🛠️ MCP Tools Manager]
    C --> F[⚙️ Config Manager]
    D --> G[🎨 Template Engine]
    E --> H[🔧 Management Tools]
    F --> I[🔥 Hot Reload System]

    style C fill:#ff6b35
    style D fill:#00ff88
    style E fill:#0066cc

🌐 MCP Client Compatibility

This server implements the Model Context Protocol (MCP) standard and works with any compatible client:

💡 Developer Note: As MCP adoption grows, this server will work with any new MCP-compatible AI assistant or development environment without modification.

🛠️ Advanced Configuration

⚙️ Server Powerhouse (config.json)

Fine-tune your server's behavior:

{
  "server": {
    "name": "Claude Custom Prompts MCP Server",
    "version": "1.0.0",
    "port": 9090
  },
  "prompts": {
    "file": "promptsConfig.json",
    "registrationMode": "name"
  },
  "transports": {
    "default": "stdio",
    "sse": { "enabled": false },
    "stdio": { "enabled": true }
  }
}

🗂️ Prompt Organization (promptsConfig.json)

Structure your AI command library:

{
  "categories": [
    {
      "id": "development",
      "name": "🔧 Development",
      "description": "Code review, debugging, and development workflows"
    },
    {
      "id": "analysis",
      "name": "📊 Analysis",
      "description": "Content analysis and research prompts"
    },
    {
      "id": "creative",
      "name": "🎨 Creative",
      "description": "Content creation and creative writing"
    }
  ],
  "imports": [
    "prompts/development/prompts.json",
    "prompts/analysis/prompts.json",
    "prompts/creative/prompts.json"
  ]
}

🚀 Advanced Features

# Research Analysis Chain

## User Message Template

Research {{topic}} and provide {{analysis_type}} analysis.

## Chain Configuration

Steps: research → extract → analyze → summarize
Input Mapping: {topic} → {content} → {key_points} → {insights}
Output Format: Structured report with executive summary

# {{ title | title }} Analysis

## Context
{% if previous_analysis %}
Building upon previous analysis: {{ previous_analysis | summary }}
{% endif %}

## Requirements
{% for req in requirements %}
{{loop.index}}. **{{req.priority | upper}}**: {{req.description}}
   {% if req.examples %}
   Examples: {% for ex in req.examples %}{{ex}}{% if not loop.last %}, {% endif %}{% endfor %}
   {% endif %}
{% endfor %}

## Focus Areas
{% set focus_areas = focus.split(',') %}
{% for area in focus_areas %}
- {{ area | trim | title }}
{% endfor %}

# Update prompts on-the-fly
>>update_prompt id="analysis_prompt" content="new template"

# Add new sections dynamically
>>modify_prompt_section id="research" section="examples" content="new examples"

# Hot-reload everything
>>reload_prompts reason="updated templates"

// Health Check Response
{
  healthy: true,
  modules: {
    foundation: true,
    dataLoaded: true,
    modulesInitialized: true,
    serverRunning: true
  },
  performance: {
    uptime: 86400,
    memoryUsage: { rss: 45.2, heapUsed: 23.1 },
    promptsLoaded: 127,
    categoriesLoaded: 8
  }
}

📚 Documentation Hub

🤝 Contributing

We're building the future of AI prompt engineering! Join our community:

• 🐛 Found a bug? Open an issue
• 💡 Have an idea? Start a discussion
• 🔧 Want to contribute? Check our Contributing Guide
• 📖 Need help? Visit our Documentation

📄 License

Released under the MIT License - see the file for details.