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.
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.
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
# with uv recommended
uv add mcpomni-connect
# using pip
pip install mcpomni-connect
# start the cli running the command ensure your api key is export or create .env
mcpomni_connect
# 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
tests/
├── unit/ # Unit tests for individual components
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 "LLM_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
{
"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"]
}
}
}
/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# 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"]
}
The client intelligently:
# 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
# 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
We welcome contributions! See our Contributing Guide for details.
This project is licensed under the MIT License - see the LICENSE file for details.
Built with ❤️ by the MCPOmni Connect Team