🚀 MCPOmni Connect - Universal Gateway to MCP Servers
MCPOmni Connect is a powerful, universal command-line interface (CLI) that serves as your gateway to the Model Context Protocol (MCP) ecosystem. It seamlessly integrates multiple MCP servers, AI models, and various transport protocols into a unified, intelligent interface.
✨ Key Features
🔌 Universal Connectivity
- Multi-Protocol Support
- Native support for stdio transport
- Server-Sent Events (SSE) for real-time communication
- Docker container integration
- NPX package execution
- Extensible transport layer for future protocols
🧠 AI-Powered Intelligence
- Advanced LLM Integration
- Seamless OpenAI model integration
- Dynamic system prompts based on available capabilities
- Intelligent context management
- Automatic tool selection and chaining
💬 Prompt Management
- Advanced Prompt Handling
- Dynamic prompt discovery across servers
- Flexible argument parsing (JSON and key-value formats)
- Cross-server prompt coordination
- Intelligent prompt validation
- Context-aware prompt execution
- Real-time prompt responses
- Support for complex nested arguments
- Automatic type conversion and validation
🛠️ Tool Orchestration
- Dynamic Tool Discovery & Management
- Automatic tool capability detection
- Cross-server tool coordination
- Intelligent tool selection based on context
- Real-time tool availability updates
📦 Resource Management
- Universal Resource Access
- Cross-server resource discovery
- Unified resource addressing
- Automatic resource type detection
- Smart content summarization
🔄 Server Management
- Advanced Server Handling
- Multiple simultaneous server connections
- Automatic server health monitoring
- Graceful connection management
- Dynamic capability updates
🏗️ Architecture
Core Components
MCPOmni Connect
├── Transport Layer
│ ├── Stdio Transport
│ ├── SSE Transport
│ └── Docker Integration
├── Session Management
│ ├── Multi-Server Orchestration
│ └── Connection Lifecycle Management
├── Tool Management
│ ├── Dynamic Tool Discovery
│ ├── Cross-Server Tool Routing
│ └── Tool Execution Engine
└── AI Integration
├── LLM Processing
├── Context Management
└── Response Generation
🚀 Getting Started
Prerequisites
- Python 3.12+
- OpenAI API key
- UV package manager (recommended)
Install using package manager
# with uv recommended
uv add mcpomni-connect
# using pip
pip install mcpomni-connect
Start CLI
# start the cli running the command ensure your api key is export or create .env
mcpomni_connect
Development Quick Start
Installation
Clone the repository
git clone https://github.com/Abiorh001/mcp_omni_connect.git
cd mcp_omni_connect
# Create and activate virtual environment
uv venv
source .venv/bin/activate
# Install dependencies
uv sync
Configuration
Set up environment variables
echo "OPENAI_API_KEY=your_key_here" > .env
# Configure your servers in servers_config.json
** Start Client**
Start the cient
uv run src/main.py pr python src/main.py
Server Configuration Examples
{
"LLM": {
"model": "gpt-4o-mini",
"temperature": 0.5,
"max_tokens": 5000,
"top_p": 0
},
"mcpServers": {
"filesystem-server": {
"command": "npx",
"args": [
"@modelcontextprotocol/server-filesystem",
"/path/to/files"
]
},
"sse-server": {
"type": "sse",
"url": "http://localhost:3000/mcp",
"headers": {
"Authorization": "Bearer token"
},
},
"docker-server": {
"command": "docker",
"args": ["run", "-i", "--rm", "mcp/server"]
}
}
}
🎯 Usage
Interactive Commands
/tools - List all available tools across servers
/prompts - View available prompts
/prompt:<name>/<args> - Execute a prompt with arguments
# Example: Weather prompt
/prompt:weather/location=tokyo/units=metric
Alternative JSON format
/prompt:weather/{"location":"tokyo","units":"metric"}
/resources - List available resources
/resource:<uri> - Access and analyze a resource
/debug - Toggle debug mode
/refresh - Update server capabilities
Prompt Management
# List all available prompts
/prompts
# Basic prompt usage
/prompt:weather/location=tokyo
# Prompt with multiple arguments depends on the server prompt arguments requirements
/prompt:travel-planner/from=london/to=paris/date=2024-03-25
# JSON format for complex arguments
/prompt:analyze-data/{
"dataset": "sales_2024",
"metrics": ["revenue", "growth"],
"filters": {
"region": "europe",
"period": "q1"
}
}
# Nested argument structures
/prompt:market-research/target=smartphones/criteria={
"price_range": {"min": 500, "max": 1000},
"features": ["5G", "wireless-charging"],
"markets": ["US", "EU", "Asia"]
}
Advanced Prompt Features
- Argument Validation : Automatic type checking and validation
- Default Values : Smart handling of optional arguments
- Context Awareness : Prompts can access previous conversation context
- Cross-Server Execution : Seamless execution across multiple MCP servers
- Error Handling : Graceful handling of invalid arguments with helpful messages
- Dynamic Help : Detailed usage information for each prompt
AI-Powered Interactions
The client intelligently:
- Chains multiple tools together
- Provides context-aware responses
- Automatically selects appropriate tools
- Handles errors gracefully
- Maintains conversation context
🔧 Advanced Features
Tool Orchestration
# Example of automatic tool chaining if the tool is available in the servers connected
User: "Find charging stations near Silicon Valley and check their current status"
# Client automatically:
1. Uses Google Maps API to locate Silicon Valley
2. Searches for charging stations in the area
3. Checks station status through EV network API
4. Formats and presents results
Resource Analysis
# Automatic resource processing
User: "Analyze the contents of /path/to/document.pdf"
# Client automatically:
1. Identifies resource type
2. Extracts content
3. Processes through LLM
4. Provides intelligent summary
Demo
🤝 Contributing
We welcome contributions! See our Contributing Guide for details.
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
📬 Contact & Support
Built with ❤️ by the MCPOmni Connect Team
