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MCPOmni Connect: Link OpenAI Models & Juggle Cross-Cluster Tools - MCP Implementation

MCPOmni Connect: Link OpenAI Models & Juggle Cross-Cluster Tools

πŸš€ MCPOmni Connect: Command-line mastery for MCP serversβ€”seamlessly link OpenAI models, juggle tools/resources across clusters, all via stdio. Your AI workflows just got real.

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About MCPOmni Connect

What is MCPOmni Connect: Link OpenAI Models & Juggle Cross-Cluster Tools?

MCPOmni Connect is a universal command-line interface (CLI) designed to unify access to the Model Context Protocol (MCP) ecosystem. It bridges multiple MCP servers, AI models, and transport protocols into a single intelligent interface. Key capabilities include seamless integration of OpenAI models, dynamic tool orchestration, and real-time resource management across distributed systems. This tool simplifies complex workflows by automating protocol handling, prompt execution, and cross-server collaboration.

How to Use MCPOmni Connect: Link OpenAI Models & Juggle Cross-Cluster Tools?

Getting started involves three steps: installation, configuration, and execution. Install via uv add mcpomni-connect or pip install mcpomni-connect. Configure your environment with an OpenAI API key and define servers in a JSON file specifying protocols like Docker, SSE, or NPX. Launch the CLI and use commands like /tools to list tools, /prompt:weather/location=tokyo to execute prompts, or /resources to access files and APIs. Advanced users can chain tools or debug workflows with real-time feedback.

MCPOmni Connect Features

Key Features of MCPOmni Connect: Link OpenAI Models & Juggle Cross-Cluster Tools?

  • Cross-Protocol Support: Connect via stdio, SSE, Docker, or NPX with extensible transport layers.
  • AI-Driven Workflows: Automatically chain tools like Google Maps and EV networks, or analyze PDFs with smart context summarization.
  • Smart Prompt Handling: Validate arguments, support nested JSON inputs, and execute prompts across servers with context awareness.
  • Dynamic Resource Management: Discover, analyze, and route requests across distributed servers using unified addressing.
  • Server Resilience: Monitor health, auto-reconnect, and update capabilities in real-time for uninterrupted operations.

Use Cases of MCPOmni Connect: Link OpenAI Models & Juggle Cross-Cluster Tools?

Typical scenarios include:

  • Multi-Tool Automation: Automatically chain Google Maps and EV charging APIs to fetch real-time station statuses.
  • Document Analysis: Process PDFs, extract content, and generate summaries using built-in LLM capabilities.
  • Dynamic Travel Planning: Input /prompt:travel-planner/from=london/to=paris to auto-generate itineraries with embedded API data.
  • Hybrid Environments: Manage legacy Docker-based servers alongside cloud-based SSE endpoints in a single workflow.

MCPOmni Connect FAQ

FAQ from MCPOmni Connect: Link OpenAI Models & Juggle Cross-Cluster Tools?

  • Q: Can I add custom servers?
    Yes, define new servers in JSON with "command" and "args" fields for Docker/NPX or "url" for SSE.
  • Q: What protocols are supported?
    Stdio, SSE, Docker, NPX, with extensibility for future protocols via the transport layer.
  • Q: How do I handle errors?
    Error messages include validation feedback and suggestions for correcting parameters or tool availability.
  • Q: Is OpenAI API required?
    LLM features require an OpenAI API key, but core server orchestration works without it.
  • Q: Can I debug workflows?
    Use /debug mode to trace prompt execution paths and tool dependencies in real-time.

Content

πŸš€ 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

πŸ”Œ 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

πŸ’¬ 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

πŸ› οΈ 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

πŸ“¦ Resource Management

  • Universal Resource Access
    • Cross-server resource discovery
    • Unified resource addressing
    • Automatic resource type detection
    • Smart content summarization

πŸ”„ Server Management

  • Advanced Server Handling
    • Multiple simultaneous server connections
    • Automatic server health monitoring
    • Graceful connection management
    • Dynamic capability updates

πŸ—οΈ Architecture

Core Components

MCPOmni Connect
β”œβ”€β”€ Transport Layer
β”‚   β”œβ”€β”€ Stdio Transport
β”‚   β”œβ”€β”€ SSE Transport
β”‚   └── Docker Integration
β”œβ”€β”€ Session Management
β”‚   β”œβ”€β”€ Multi-Server Orchestration
β”‚   └── Connection Lifecycle Management
β”œβ”€β”€ Tool Management
β”‚   β”œβ”€β”€ Dynamic Tool Discovery
β”‚   β”œβ”€β”€ Cross-Server Tool Routing
β”‚   └── Tool Execution Engine
└── AI Integration
    β”œβ”€β”€ LLM Processing
    β”œβ”€β”€ Context Management
    └── Response Generation

πŸš€ 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

  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
  1. Configuration

    Set up environment variables

echo "OPENAI_API_KEY=your_key_here" > .env

# Configure your servers in servers_config.json
  1. ** 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

πŸ”§ 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

mcp_client_new1-MadewithClipchamp-ezgif com-optimize

🀝 Contributing

We welcome contributions! See our Contributing Guide for details.

πŸ“„ License

This project is licensed under the MIT License - see the LICENSE file for details.

πŸ“¬ Contact & Support


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