mcp_omni_connect

by Abiorh001

MCPOmni Connect is a versatile command-line interface (CLI) client designed to connect to various Model Context Protocol (MCP) servers using stdio transport. It provides seamless integration with OpenAI models and supports dynamic tool and resource management across multiple servers.

#server #mod #mcp

Website

About

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