Claude Prompts MCP Server

🌟 What Makes This Special? (v1.3.0 - "Consolidated Architecture with Systematic Framework Application")

• 🎯 Three-Tier Execution Model → Routes between prompts (lightning-fast), templates (framework-enhanced), and chains (LLM-driven) based on file structure
• 🧠 Structural Analysis Engine → File structure analysis detects execution type (with optional W.I.P LLM-powered semantic enhancement)
• ⚡ Three-Tier Performance → From instant variable substitution to comprehensive methodology-guided processing
• 🔧 Unified Creation Tools → Create prompts or templates with type-specific optimization
• 🛡️ Intelligent Quality Gates → Framework-aware validation with conditional injection based on execution tier
• 🔄 Configurable Analysis → Structural analysis with optional semantic enhancement and manual methodology selection
• 🔥 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 with a three-tier execution architecture that routes between lightning-fast prompts, framework-enhanced templates, and LLM-driven chains based on file structure analysis 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:

# 🎯 Universal prompt execution with intelligent type detection
prompt_engine >>code_formatter language="Python" style="PEP8"
→ System detects execution tier, applies appropriate processing automatically

# 📋 Create and manage prompts with intelligent analysis
prompt_manager create name="code_reviewer" type="template" \
  content="Analyze {{code}} for security, performance, and maintainability"
→ Creates framework-enhanced template with CAGEERF methodology integration

# 🔍 Analyze existing prompts for execution optimization
prompt_manager analyze_type prompt_id="my_prompt"
→ Shows: "Type: template, Framework: CAGEERF, Confidence: 85%, Gates: enabled"

# ⚙️ System control and framework management
system_control switch_framework framework="ReACT" reason="Problem-solving focus"
→ Switches active methodology with performance monitoring

# 🔥 Execute with full three-tier intelligence
prompt_engine >>analysis_chain input="complex research data" llm_driven_execution=true
→ LLM-driven chain execution with step-by-step coordination (requires semantic LLM integration)

🌟 Why This Architecture Matters:

• 🧠 Structural Intelligence: File structure analysis provides reliable execution routing with minimal configuration
• 🔄 Dynamic Capability Building: Build and extend your AI assistant's capabilities through conversational prompt management
• 🎮 Reduced Friction: Minimal configuration required - execution type detected from file structure
• ⚡ Systematic Workflow: Create → Structure-based routing → Framework application in a reliable flow
• 🧠 Intelligent Command Routing: Built-in command detection with multi-strategy parsing and automatic tool routing
• 🧠 Sophisticated Methodology System: Four proven thinking frameworks (CAGEERF, ReACT, 5W1H, SCAMPER) with manual selection and conditional application

This is what well-architected AI infrastructure looks like – where systematic analysis and proven methodologies enhance your AI interactions through structured approaches rather than magic.

🧠 Advanced Framework System

🎯 Revolutionary Methodology Integration

The server features a sophisticated framework system that brings structured thinking methodologies to your AI interactions:

🎨 Four Intelligent Methodologies

• 🔍 CAGEERF: Comprehensive structured approach (Context, Analysis, Goals, Execution, Evaluation, Refinement, Framework)
• 🧠 ReACT: Reasoning and Acting pattern for systematic problem-solving
• ❓ 5W1H: Who, What, When, Where, Why, How systematic analysis
• 🚀 SCAMPER: Creative problem-solving (Substitute, Combine, Adapt, Modify, Put to other uses, Eliminate, Reverse)

⚙️ Intelligent Framework Features

• 🧠 Manual Selection: Choose optimal methodology manually based on your needs, with sophisticated conditional application
• 🔄 Runtime Switching: Change active framework with performance monitoring and seamless transition
• ⚡ Conditional Injection: Framework enhancement applied only when beneficial (bypassed for simple prompts)
• 📊 Switching Performance: Monitor framework switching mechanics and performance

# 🔄 Switch methodology for different thinking approaches
system_control switch_framework framework="ReACT" reason="Problem-solving focus"
→ Switches to ReACT methodology with performance monitoring

# 📊 Monitor framework performance and usage
system_control analytics show_details=true
→ View framework switching history and performance metrics

# ⚙️ Get current framework status
system_control status
→ Shows active framework, available methodologies, and system health

🎆 The Result: Your AI conversations become more structured, thoughtful, and effective through proven thinking methodologies applied systematically based on your chosen framework.

⚠️ Analysis System Capabilities

🗓️ What the System Actually Does:

• 📝 Structural Analysis: Detects execution type by examining template variables ({{variable}}), chain steps, and file structure
• 🔄 Framework Application: Applies manually selected framework methodology (CAGEERF, ReACT, 5W1H, SCAMPER) based on execution tier
• ⚡ Routing Logic: Routes to appropriate execution tier (prompt/template/chain) based on structural characteristics

🧠 Optional Semantic Enhancement:

• LLM Integration: When enabled, provides true semantic understanding of prompt content
• Advanced Analysis: Intelligent methodology recommendations and complexity assessment
• Default Mode: Structural analysis only - honest about limitations without LLM access

🎯 Manual Framework Control:

# Framework selection is manual, not automatic
system_control switch_framework framework="ReACT" reason="Problem-solving focus"

⚡ 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\\prompts\\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 (Optional): MCP_PROMPTS_CONFIG_PATH=path/to/prompts/promptsConfig.json

Claude Code CLI Installation

For Claude Code CLI users, use the one-command installation:

claude mcp add-json claude-prompts-mcp '{"type":"stdio","command":"node","args":["path/to/claude-prompts-mcp/server/dist/index.js"],"env":{}}'

💡 Pro Tip: Environment variables are optional - the server auto-detects paths in 99% of cases. Use absolute paths for guaranteed compatibility across all MCP clients!

🎮 Start Building Immediately (v1.3.0 Consolidated Architecture)

Your AI command arsenal is ready with enhanced reliability:

# 🧠 Discover your intelligent superpowers
prompt_manager list filter="category:analysis"
→ Intelligent filtering shows relevant prompts with usage examples

# 🎯 Structural execution routing - system detects execution type from file structure
prompt_engine >>friendly_greeting name="Developer"
→ Detected as template (has {{variables}}), returns framework-enhanced greeting

prompt_engine >>content_analysis input="my research data"
→ Detected as template (structural analysis), applies framework injection, executes with quality gates

prompt_engine >>analysis_chain input="my content" llm_driven_execution=true
→ Detected as chain (has chainSteps), provides LLM-driven step-by-step execution (requires semantic LLM integration)

# 📊 Monitor intelligent detection performance
system_control 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"
→ Prompt created via conversation, system detects execution type from structure, applies active framework

# 🔄 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 LLM-driven chain 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)
prompt_engine >>content_analysis input="sensitive data" step_confirmation=true gate_validation=true
→ Force step confirmation for sensitive analysis

🌟 The Architecture: Your prompt library becomes a structured extension of your workflow, organized and enhanced through systematic methodology application.

🔥 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,
  "executionMode": "chain",
  "chainSteps": [
    {
      "stepName": "Extract Key Points",
      "promptId": "extract_key_points",
      "inputMapping": { "content": "original_content" },
      "outputMapping": { "key_points": "extracted_points" },
      "executionType": "template"
    },
    {
      "stepName": "Analyze Sentiment",
      "promptId": "sentiment_analysis",
      "inputMapping": { "text": "extracted_points" },
      "outputMapping": { "sentiment": "analysis_result" },
      "executionType": "template"
    }
  ]
}

📊 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 with intelligent re-analysis
prompt_manager update id="analysis_prompt" content="new template"
→ System re-analyzes execution type and framework requirements

# Modify specific sections with validation
prompt_manager modify id="research" section="examples" content="new examples"
→ Section updated with automatic template validation

# Hot-reload with comprehensive validation
system_control reload reason="updated templates"
→ Full system reload with health monitoring

// 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.