🚀 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 models integration
  • Seamless OpenRouter models integration
  • Seamless Groq models integration
  • Dynamic system prompts based on available capabilities
  • Intelligent context management
  • Automatic tool selection and chaining
  • Universal model support through custom ReAct Agent
    • Handles models without native function calling
    • Dynamic function execution based on user requests
    • Intelligent tool orchestration

🔒 Security & Privacy

• Explicit User Control
  • All tool executions require explicit user approval
  • Clear explanation of tool actions before execution
  • Transparent disclosure of data access and usage
• Data Protection
  • Strict data access controls
  • Server-specific data isolation
  • No unauthorized data exposure
• Privacy-First Approach
  • Minimal data collection
  • User data remains on specified servers
  • No cross-server data sharing without consent
• Secure Communication
  • Encrypted transport protocols
  • Secure API key management
  • Environment variable protection

💬 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 3.10+
• LLM 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

🧪 Testing

Running Tests

# Run all tests with verbose output
pytest tests/ -v

# Run specific test file
pytest tests/test_specific_file.py -v

# Run tests with coverage report
pytest tests/ --cov=src --cov-report=term-missing

Test Structure

tests/
├── unit/           # Unit tests for individual components

Development Quick Start

1. 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
   

2. Configuration

   # Set up environment variables
   echo "LLM_API_KEY=your_key_here" > .env
   
   # Configure your servers in servers_config.json
   

3. ** Start Client**

   # Start the cient
   uv run src/main.py pr python src/main.py
   

Server Configuration Examples

{   
    "LLM": {
        "provider": "openai",  // Supports: "openai", "openrouter", "groq"
        "model": "gpt-4",      // Any model from supported providers
        "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

Model Support

• OpenAI Models
  • Full support for all OpenAI models
  • Native function calling for compatible models
  • ReAct Agent fallback for older models
• OpenRouter Models
  • Access to all OpenRouter-hosted models
  • Unified interface for model interaction
  • Automatic capability detection
• Groq Models
  • Support for all Groq models
  • Ultra-fast inference capabilities
  • Seamless integration with tool system
• Universal Model Support
  • Custom ReAct Agent for models without function calling
  • Dynamic tool execution based on model capabilities
  • Intelligent fallback mechanisms

🔧 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

• Author: Abiola Adeshina
• Email: abioladedayo1993@gmail.com
• GitHub Issues: Report a bug

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