🚀 The Universal Model Context Protocol Server for Any MCP Client
Supercharge your AI workflows with battle-tested prompt engineering, intelligent orchestration, and lightning-fast hot-reload capabilities. Works seamlessly with Claude Desktop, Cursor Windsurf, and any MCP-compatible client.
⚡ Quick Start • 🎯 Features • 📚 Docs • 🛠️ Advanced
- 🧠 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.
🎯 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.
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🎯 Developer Experience
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🚀 Enterprise Architecture
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🛠️ Enhanced MCP Tools Suite (v1.1.0)
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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 startDrop 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"
}
}
}
}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!
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.
⚡ Lightning-Fast Hot-Reload → Edit prompts, see changes instantly
Our sophisticated orchestration engine monitors your files and reloads everything seamlessly:
# Edit any prompt file → Server detects → Reloads automatically → Zero downtime- Instant Updates: Change templates, arguments, descriptions in real-time
- Zero Restart Required: Advanced hot-reload system keeps everything running
- Smart Dependency Tracking: Only reloads what actually changed
- Graceful Error Recovery: Invalid changes don't crash the server
🎨 Next-Gen Template Engine → Nunjucks-powered dynamic prompts
Go beyond simple text replacement with a full template engine:
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 %}- Conditional Logic: Smart prompts that adapt based on input
- Loops & Iteration: Handle arrays and complex data structures
- Template Inheritance: Reuse and extend prompt patterns
- Real-Time Processing: Templates render with live data injection
🏗️ Enterprise-Grade Orchestration → Multi-phase startup with health monitoring
Built like production software with comprehensive architecture:
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- Dependency Management: Modules start in correct order with validation
- Health Monitoring: Real-time status of all components
- Performance Metrics: Memory usage, uptime, connection tracking
- Diagnostic Tools: Built-in troubleshooting and debugging
🔄 Intelligent Prompt Chains → Multi-step AI workflows
Create sophisticated workflows where each step builds on the previous:
{
"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" }
}
]
}- Visual Step Planning: See your workflow before execution
- Input/Output Mapping: Data flows seamlessly between steps
- Error Recovery: Failed steps don't crash the entire chain
- Flexible Execution: Run chains or individual steps as needed
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
This server implements the Model Context Protocol (MCP) standard and works with any compatible client:
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✅ Tested & Verified
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🔌 Transport Support
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🎯 Integration Features
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💡 Developer Note: As MCP adoption grows, this server will work with any new MCP-compatible AI assistant or development environment without modification.
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 }
}
}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"
]
}🔄 Multi-Step Prompt Chains → Build sophisticated AI workflows
Create complex workflows that chain multiple prompts together:
# 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 summaryCapabilities:
- Sequential Processing: Each step uses output from previous step
- Parallel Execution: Run multiple analysis streams simultaneously
- Error Recovery: Graceful handling of failed steps
- Custom Logic: Conditional branching based on intermediate results
🎨 Advanced Template Features → Dynamic, intelligent prompts
Leverage the full power of Nunjucks templating:
# {{ 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 %}Template Features:
- Filters & Functions: Transform data on-the-fly
- Conditional Logic: Smart branching based on input
- Loops & Iteration: Handle complex data structures
- Template Inheritance: Build reusable prompt components
🔧 Real-Time Management Tools → Hot management without downtime
Manage your prompts dynamically while the server runs:
# 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"Management Capabilities:
- Live Updates: Change prompts without server restart
- Section Editing: Modify specific parts of prompts
- Bulk Operations: Update multiple prompts at once
- Rollback Support: Undo changes when things go wrong
📊 Production Monitoring → Enterprise-grade observability
Built-in monitoring and diagnostics for production environments:
// 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
}
}Monitoring Features:
- Real-Time Health Checks: All modules continuously monitored
- Performance Metrics: Memory, uptime, connection tracking
- Diagnostic Tools: Comprehensive troubleshooting information
- Error Tracking: Graceful error handling with detailed logging
| Guide | Description |
|---|---|
| 📥 Installation Guide | Complete setup walkthrough with troubleshooting |
| 🛠️ Troubleshooting Guide | Common issues, diagnostic tools, and solutions |
| 🏗️ Architecture Overview | A deep dive into the orchestration engine, modules, and data flow |
| 📝 Prompt Format Guide | Master prompt creation with examples |
| 🔗 Chain Execution Guide | Build complex multi-step workflows |
| ⚙️ Prompt Management | Dynamic management and hot-reload features |
| 🚀 MCP Tools Reference | Complete MCP tools documentation |
| 🗺️ Roadmap & TODO | Planned features and development roadmap |
| 🤝 Contributing | Join our development community |
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
Released under the MIT License - see the file for details.
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