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mcp_omni_connect

by Abiorh001/mcp_omni_connect

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MCPOmni Connect is an agent execution runtime that connects to multiple MCP servers via stdio, SSE, or streamable HTTP. It supports chat, autonomous agents, and planner-based orchestration treating each MCP server as a tool agent to enable dynamic multi-agent workflows across LLM systems, and can be embedded in FastAPI or backend systems.

automationpythonChat & Messaging

🚀 MCPOmni Connect - Universal Gateway to MCP Servers

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MCPOmni Connect is a powerful, intelligent AI agent system and universal command-line interface (CLI) that goes beyond being just a gateway to the Model Context Protocol (MCP) ecosystem. It acts as an autonomous agent through its ReAct Agent Mode and Orchestrator Mode, capable of independent reasoning, decision-making, and complex task execution. It seamlessly integrates multiple MCP servers, AI models, and various transport protocols into a unified, intelligent interface that can operate autonomously or interactively.

🚀 New User? Start with the ⚙️ Configuration Guide to understand the difference between config files, transport types, and OAuth behavior. Then check out the 🧪 Testing section to get started quickly.

✨ Key Features

🤖 Intelligent Agent System

  • ReAct Agent Mode
    • Autonomous task execution with reasoning and action cycles
    • Independent decision-making without human intervention
    • Advanced problem-solving through iterative reasoning
    • Self-guided tool selection and execution
    • Complex task decomposition and handling
  • Orchestrator Agent Mode
    • Strategic multi-step task planning and execution
    • Intelligent coordination across multiple MCP servers
    • Dynamic agent delegation and communication
    • Parallel task execution when possible
    • Sophisticated workflow management with real-time progress monitoring
  • Interactive Chat Mode
    • Human-in-the-loop task execution with approval workflows
    • Step-by-step guidance and explanations
    • Educational mode for understanding AI decision processes

🔌 Universal Connectivity

  • Multi-Protocol Support
    • Native support for stdio transport
    • Server-Sent Events (SSE) for real-time communication
    • Streamable HTTP for efficient data streaming
    • Docker container integration
    • NPX package execution
    • Extensible transport layer for future protocols
  • Authentication Support
    • OAuth 2.0 authentication flow
    • Bearer token authentication
    • Custom header support
    • Secure credential management
  • Agentic Operation Modes
    • Seamless switching between chat, autonomous, and orchestrator modes
    • Context-aware mode selection based on task complexity
    • Persistent state management across mode transitions

🧠 AI-Powered Intelligence

  • Unified LLM Integration with LiteLLM
    • Single unified interface for all AI providers
    • Support for 100+ models across providers including:
      • OpenAI (GPT-4, GPT-3.5, etc.)
      • Anthropic (Claude 3.5 Sonnet, Claude 3 Haiku, etc.)
      • Google (Gemini Pro, Gemini Flash, etc.)
      • Groq (Llama, Mixtral, Gemma, etc.)
      • DeepSeek (DeepSeek-V3, DeepSeek-Coder, etc.)
      • Azure OpenAI
      • OpenRouter (access to 200+ models)
      • Ollama (local models)
    • Simplified configuration and reduced complexity
    • 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 in chat mode
    • 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

💾 Memory Management

  • Redis-Powered Persistence
    • Long-term conversation memory storage
    • Session persistence across restarts
    • Configurable memory retention
    • Easy memory toggle with commands
  • Chat History File Storage
    • Save complete chat conversations to files
    • Load previous conversations from saved files
    • Continue conversations from where you left off
    • Persistent chat history across sessions
    • File-based backup and restoration of conversations
  • Intelligent Context Management
    • Automatic context pruning
    • Relevant information retrieval
    • Memory-aware responses
    • Cross-session context maintenance

💬 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
  • Client-Side Sampling Support
    • Dynamic sampling configuration from client
    • Flexible LLM response generation
    • Customizable sampling parameters
    • Real-time sampling adjustments

🛠️ 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
    • Flexible authentication methods
    • Runtime server configuration 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)
  • Redis server (optional, for persistent memory)

Install using package manager

# with uv recommended
uv add mcpomni-connect
# using pip
pip install mcpomni-connect

Configuration

# Set up environment variables
echo "LLM_API_KEY=your_key_here" > .env
# Optional: Configure Redis connection
echo "REDIS_HOST=localhost" >> .env
echo "REDIS_PORT=6379" >> .env
echo "REDIS_DB=0" >> .env"
# Configure your servers in servers_config.json

⚙️ Configuration Guide

Configuration Files Overview

MCPOmni Connect uses two separate configuration files for different purposes:

1. .env File - Environment Variables

Contains sensitive information like API keys and optional settings:

# Required: Your LLM provider API key
LLM_API_KEY=your_api_key_here

# Optional: Redis configuration (for persistent memory)
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_DB=0



#### 2. `servers_config.json` - Server & Agent Configuration
Contains application settings, LLM configuration, and MCP server connections:

```json
{
    "AgentConfig": {
        "tool_call_timeout": 30,
        "max_steps": 15,
        "request_limit": 1000,
        "total_tokens_limit": 100000
    },
    "LLM": {
        "provider": "openai",
        "model": "gpt-4o-mini",
        "temperature": 0.5,
        "max_tokens": 5000,
        "top_p": 0.7
    },
    "mcpServers": {
        "your-server-name": {
            "transport_type": "stdio",
            "command": "uvx",
            "args": ["mcp-server-package"]
        }
    }
}

🚦 Transport Types & Authentication

MCPOmni Connect supports multiple ways to connect to MCP servers:

1. stdio - Direct Process Communication

Use when: Connecting to local MCP servers that run as separate processes

{
    "server-name": {
        "transport_type": "stdio",
        "command": "uvx",
        "args": ["mcp-server-package"]
    }
}
  • No authentication needed
  • No OAuth server started
  • Most common for local development

2. sse - Server-Sent Events

Use when: Connecting to HTTP-based MCP servers using Server-Sent Events

{
    "server-name": {
        "transport_type": "sse",
        "url": "http://your-server.com:4010/sse",
        "headers": {
            "Authorization": "Bearer your-token"
        },
        "timeout": 60,
        "sse_read_timeout": 120
    }
}
  • Uses Bearer token or custom headers
  • No OAuth server started

3. streamable_http - HTTP with Optional OAuth

Use when: Connecting to HTTP-based MCP servers with or without OAuth

Without OAuth (Bearer Token):

{
    "server-name": {
        "transport_type": "streamable_http",
        "url": "http://your-server.com:4010/mcp",
        "headers": {
            "Authorization": "Bearer your-token"
        },
        "timeout": 60
    }
}
  • Uses Bearer token or custom headers
  • No OAuth server started

With OAuth:

{
    "server-name": {
        "transport_type": "streamable_http",
        "auth": {
            "method": "oauth"
        },
        "url": "http://your-server.com:4010/mcp"
    }
}
  • OAuth callback server automatically starts on http://localhost:3000
  • This is hardcoded and cannot be changed
  • Required for OAuth flow to work properly

🔐 OAuth Server Behavior

Important: When using OAuth authentication, MCPOmni Connect automatically starts an OAuth callback server.

What You'll See:

🖥️  Started callback server on http://localhost:3000

Key Points:

  • This is normal behavior - not an error
  • The address http://localhost:3000 is hardcoded and cannot be changed
  • The server only starts when you have "auth": {"method": "oauth"} in your config
  • The server stops when the application shuts down
  • Only used for OAuth token handling - no other purpose

When OAuth is NOT Used:

  • Remove the entire "auth" section from your server configuration
  • Use "headers" with "Authorization": "Bearer token" instead
  • No OAuth server will start

🛠️ Troubleshooting Common Issues

"Failed to connect to server: Session terminated"

Possible Causes & Solutions:

  1. Wrong Transport Type

    Problem: Your server expects 'stdio' but you configured 'streamable_http'
Solution: Check your server's documentation for the correct transport type

  2. OAuth Configuration Mismatch

    Problem: Your server doesn't support OAuth but you have "auth": {"method": "oauth"}
Solution: Remove the "auth" section entirely and use headers instead:

"headers": {
    "Authorization": "Bearer your-token"
}

  3. Server Not Running

    Problem: The MCP server at the specified URL is not running
Solution: Start your MCP server first, then connect with MCPOmni Connect

  4. Wrong URL or Port

    Problem: URL in config doesn't match where your server is running
Solution: Verify the server's actual address and port

"Started callback server on http://localhost:3000" - Is This Normal?

Yes, this is completely normal when:

  • You have "auth": {"method": "oauth"} in any server configuration
  • The OAuth server handles authentication tokens automatically
  • You cannot and should not try to change this address

If you don't want the OAuth server:

  • Remove "auth": {"method": "oauth"} from all server configurations
  • Use alternative authentication methods like Bearer tokens

📋 Configuration Examples by Use Case

Local Development (stdio)

{
    "mcpServers": {
        "local-tools": {
            "transport_type": "stdio",
            "command": "uvx",
            "args": ["mcp-server-tools"]
        }
    }
}

Remote Server with Token

{
    "mcpServers": {
        "remote-api": {
            "transport_type": "streamable_http",
            "url": "http://api.example.com:8080/mcp",
            "headers": {
                "Authorization": "Bearer abc123token"
            }
        }
    }
}

Remote Server with OAuth

{
    "mcpServers": {
        "oauth-server": {
            "transport_type": "streamable_http",
            "auth": {
                "method": "oauth"
            },
            "url": "http://oauth-server.com:8080/mcp"
        }
    }
}

Start CLI

# start the cli running the command ensure your api key is exported 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 client
uv run run.py
# or
python run.py

🧑‍💻 Examples

Basic CLI Example

You can run the basic CLI example to interact with MCPOmni Connect directly from the terminal.

Using uv (recommended):

uv run examples/basic.py

Or using Python directly:

python examples/basic.py

FastAPI Server Example

You can also run MCPOmni Connect as a FastAPI server for web or API-based interaction.

Using uv:

uv run examples/fast_api_iml.py

Or using Python directly:

python examples/fast_api_iml.py

Web Client

A simple web client is provided in examples/index.html.

  • Open it in your browser after starting the FastAPI server.
  • It connects to http://localhost:8000 and provides a chat interface.
  • The FastAPI server will start on http://localhost:8000 by default.
  • You can interact with the API (see examples/index.html for a simple web client).

FastAPI API Endpoints

/chat/agent_chat (POST)

  • Description: Send a chat query to the agent and receive a streamed response.
  • Request: 
    {
  "query": "Your question here",
  "chat_id": "unique-chat-id"
}
  • Response: Streamed JSON lines, each like: 
    {
  "message_id": "...",
  "usid": "...",
  "role": "assistant",
  "content": "Response text",
  "meta": [],
  "likeordislike": null,
  "time": "2024-06-10 12:34:56"
}

🛠️ Developer Integration

MCPOmni Connect is not just a CLI tool—it's also a powerful Python library that you can use to build your own backend services, custom clients, or API servers.

Build Your Own MCP Client

You can import MCPOmni Connect in your Python project to:

  • Connect to one or more MCP servers
  • Choose between ReAct Agent mode (autonomous tool use) or Orchestrator Agent mode (multi-step, multi-server planning)
  • Manage memory, context, and tool orchestration
  • Expose your own API endpoints (e.g., with FastAPI, Flask, etc.)

Example: Custom Backend with FastAPI

See examples/fast_api_iml.py for a full-featured example.

Minimal Example:

from mcpomni_connect.client import Configuration, MCPClient
from mcpomni_connect.llm import LLMConnection
from mcpomni_connect.agents.react_agent import ReactAgent
from mcpomni_connect.agents.orchestrator import OrchestratorAgent

config = Configuration()
client = MCPClient(config)
llm_connection = LLMConnection(config)

# Choose agent mode
agent = ReactAgent(...)  # or OrchestratorAgent(...)

# Use in your API endpoint
response = await agent.run(
    query="Your user query",
    sessions=client.sessions,
    llm_connection=llm_connection,
    # ...other arguments...
)

FastAPI Integration

You can easily expose your MCP client as an API using FastAPI.
See the FastAPI example for:

  • Async server startup and shutdown
  • Handling chat requests with different agent modes
  • Streaming responses to clients

Key Features for Developers:

  • Full control over agent configuration and limits
  • Support for both chat and autonomous agentic modes
  • Easy integration with any Python web framework

Server Configuration Examples

Basic OpenAI Configuration

{
    "AgentConfig": {
        "tool_call_timeout": 30,
        "max_steps": 15,
        "request_limit": 1000,
        "total_tokens_limit": 100000
    },
    "LLM": {
        "provider": "openai",
        "model": "gpt-4",
        "temperature": 0.5,
        "max_tokens": 5000,
        "max_context_length": 30000,
        "top_p": 0
    },
    "mcpServers": {
        "ev_assistant": {
            "transport_type": "streamable_http",
            "auth": {
                "method": "oauth"
            },
            "url": "http://localhost:8000/mcp"
        },
        "sse-server": {
            "transport_type": "sse",
            "url": "http://localhost:3000/sse",
            "headers": {
                "Authorization": "Bearer token"
            },
            "timeout": 60, 
            "sse_read_timeout": 120
        },
        "streamable_http-server": {
            "transport_type": "streamable_http",
            "url": "http://localhost:3000/mcp",
            "headers": {
                "Authorization": "Bearer token"
            },
            "timeout": 60, 
            "sse_read_timeout": 120
        }
    }
}

Anthropic Claude Configuration

{
    "LLM": {
        "provider": "anthropic",
        "model": "claude-3-5-sonnet-20241022",
        "temperature": 0.7,
        "max_tokens": 4000,
        "max_context_length": 200000,
        "top_p": 0.95
    }
}

Groq Configuration

{
    "LLM": {
        "provider": "groq",
        "model": "llama-3.1-8b-instant",
        "temperature": 0.5,
        "max_tokens": 2000,
        "max_context_length": 8000,
        "top_p": 0.9
    }
}

Azure OpenAI Configuration

{
    "LLM": {
        "provider": "azureopenai",
        "model": "gpt-4",
        "temperature": 0.7,
        "max_tokens": 2000,
        "max_context_length": 100000,
        "top_p": 0.95,
        "azure_endpoint": "https://your-resource.openai.azure.com",
        "azure_api_version": "2024-02-01",
        "azure_deployment": "your-deployment-name"
    }
}

Ollama Local Model Configuration

{
    "LLM": {
        "provider": "ollama",
        "model": "llama3.1:8b",
        "temperature": 0.5,
        "max_tokens": 5000,
        "max_context_length": 100000,
        "top_p": 0.7,
        "ollama_host": "http://localhost:11434"
    }
}

OpenRouter Configuration

{
    "LLM": {
        "provider": "openrouter",
        "model": "anthropic/claude-3.5-sonnet",
        "temperature": 0.7,
        "max_tokens": 4000,
        "max_context_length": 200000,
        "top_p": 0.95
    }
}

🔐 Authentication Methods

MCPOmni Connect supports multiple authentication methods for secure server connections:

OAuth 2.0 Authentication

{
    "server_name": {
        "transport_type": "streamable_http",
        "auth": {
            "method": "oauth"
        },
        "url": "http://your-server/mcp"
    }
}

Bearer Token Authentication

{
    "server_name": {
        "transport_type": "streamable_http",
        "headers": {
            "Authorization": "Bearer your-token-here"
        },
        "url": "http://your-server/mcp"
    }
}

Custom Headers

{
    "server_name": {
        "transport_type": "streamable_http",
        "headers": {
            "X-Custom-Header": "value",
            "Authorization": "Custom-Auth-Scheme token"
        },
        "url": "http://your-server/mcp"
    }
}

🔄 Dynamic Server Configuration

MCPOmni Connect supports dynamic server configuration through commands:

Add New Servers

# Add one or more servers from a configuration file
/add_servers:path/to/config.json

The configuration file can include multiple servers with different authentication methods:

{
    "new-server": {
        "transport_type": "streamable_http",
        "auth": {
            "method": "oauth"
        },
        "url": "http://localhost:8000/mcp"
    },
    "another-server": {
        "transport_type": "sse",
        "headers": {
            "Authorization": "Bearer token"
        },
        "url": "http://localhost:3000/sse"
    }
}

Remove Servers

# Remove a server by its name
/remove_server:server_name

🎯 Usage

Interactive Commands

  • /tools - List all available tools across servers
  • /prompts - View available prompts
  • /prompt:<name>/<args> - Execute a prompt with arguments
  • /resources - List available resources
  • /resource:<uri> - Access and analyze a resource
  • /debug - Toggle debug mode
  • /refresh - Update server capabilities
  • /memory - Toggle Redis memory persistence (on/off)
  • /mode:auto - Switch to autonomous agentic mode
  • /mode:chat - Switch back to interactive chat mode
  • /add_servers:<config.json> - Add one or more servers from a configuration file
  • /remove_server:<server_name> - Remove a server by its name

Memory and Chat History

# Enable Redis memory persistence
/memory

# Check memory status
Memory persistence is now ENABLED using Redis

# Disable memory persistence
/memory

# Check memory status
Memory persistence is now DISABLED

Operation Modes

# Switch to autonomous mode
/mode:auto

# System confirms mode change
Now operating in AUTONOMOUS mode. I will execute tasks independently.

# Switch back to chat mode
/mode:chat

# System confirms mode change
Now operating in CHAT mode. I will ask for approval before executing tasks.

Mode Differences

  • Chat Mode (Default)

    • Requires explicit approval for tool execution
    • Interactive conversation style
    • Step-by-step task execution
    • Detailed explanations of actions

  • Autonomous Mode

    • Independent task execution
    • Self-guided decision making
    • Automatic tool selection and chaining
    • Progress updates and final results
    • Complex task decomposition
    • Error handling and recovery

  • Orchestrator Mode

    • Advanced planning for complex multi-step tasks
    • Strategic delegation across multiple MCP servers
    • Intelligent agent coordination and communication
    • Parallel task execution when possible
    • Dynamic resource allocation
    • Sophisticated workflow management
    • Real-time progress monitoring across agents
    • Adaptive task prioritization

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 with LiteLLM

  • Unified Model Access
    • Single interface for 100+ models across all major providers
    • Automatic provider detection and routing
    • Consistent API regardless of underlying provider
    • Native function calling for compatible models
    • ReAct Agent fallback for models without function calling
  • Supported Providers
    • OpenAI: GPT-4, GPT-3.5, and all model variants
    • Anthropic: Claude 3.5 Sonnet, Claude 3 Haiku, Claude 3 Opus
    • Google: Gemini Pro, Gemini Flash, PaLM models
    • Groq: Ultra-fast inference for Llama, Mixtral, Gemma
    • DeepSeek: DeepSeek-V3, DeepSeek-Coder, and specialized models
    • Azure OpenAI: Enterprise-grade OpenAI models
    • OpenRouter: Access to 200+ models from various providers
    • Ollama: Local model execution with privacy
  • Advanced Features
    • Automatic model capability detection
    • Dynamic tool execution based on model features
    • Intelligent fallback mechanisms
    • Provider-specific optimizations

Token & Usage Management

MCPOmni Connect now provides advanced controls and visibility over your API usage and resource limits.

View API Usage Stats

Use the /api_stats command to see your current usage:

/api_stats

This will display:

  • Total tokens used
  • Total requests made
  • Total response tokens
  • Number of requests

Set Usage Limits

You can set limits to automatically stop execution when thresholds are reached:

  • Total Request Limit:
    Set the maximum number of requests allowed in a session.
  • Total Token Usage Limit:
    Set the maximum number of tokens that can be used.
  • Tool Call Timeout:
    Set the maximum time (in seconds) a tool call can take before being terminated.
  • Max Steps:
    Set the maximum number of steps the agent can take before stopping.

You can configure these in your servers_config.json under the AgentConfig section:

"AgentConfig": {
    "tool_call_timeout": 30,        // Tool call timeout in seconds
    "max_steps": 15,                // Max number of steps before termination
    "request_limit": 1000,          // Max number of requests allowed
    "total_tokens_limit": 100000    // Max number of tokens allowed
}
  • When any of these limits are reached, the agent will automatically stop running and notify you.

Example Commands

# Check your current API usage and limits
/api_stats

# Set a new request limit (example)
# (This can be done by editing servers_config.json or via future CLI commands)

🔧 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

🔍 Troubleshooting

📖 For comprehensive configuration help, see the ⚙️ Configuration Guide section above, which covers:

  • Config file differences (.env vs servers_config.json)
  • Transport type selection and authentication
  • OAuth server behavior explanation
  • Common connection issues and solutions

Common Issues and Solutions

  1. Connection Issues

    Error: Could not connect to MCP server
    • Check if the server is running
    • Verify server configuration in servers_config.json
    • Ensure network connectivity
    • Check server logs for errors
    • See Transport Types & Authentication for detailed setup

  2. API Key Issues

    Error: Invalid API key
    • Verify API key is correctly set in .env
    • Check if API key has required permissions
    • Ensure API key is for correct environment (production/development)
    • See Configuration Files Overview for correct setup

  3. Redis Connection

    Error: Could not connect to Redis
    • Verify Redis server is running
    • Check Redis connection settings in .env
    • Ensure Redis password is correct (if configured)

  4. Tool Execution Failures

    Error: Tool execution failed
    • Check tool availability on connected servers
    • Verify tool permissions
    • Review tool arguments for correctness

Debug Mode

Enable debug mode for detailed logging:

/debug

For additional support, please:

  1. Check the Issues page
  2. Review closed issues for similar problems
  3. Open a new issue with detailed information if needed

🤝 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

Install

{
  "mcpServers": {
    "mcpomniconnect": {
      "command": "uvx",
      "args": [
        "mcp-server-package"
      ]
    }
  }
}
For more configuration details, refer to the content on the left

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