AI Master Control Program (MCP) Server

AI Master Control Program (MCP) Server

The AI MCP Server enables AI models, including locally hosted models with Ollama and Claude Desktop, to interact with your computer system. It acts as a bridge that allows AI models to:

• Execute system commands
• Create, read, update, and delete files
• Control other programs
• Communicate with each other

Architecture

The system consists of:

1. MCP Server: Central server that processes requests from AI models
2. Client Library: Enables easy integration with AI models
3. Model Connectors: Interfaces with various AI model backends (Ollama, Claude Desktop, etc.)
4. Task Execution Engine: Performs system operations and program control

Installation

Prerequisites

• Python 3.8+
• Ollama (optional, for local model hosting)
• Claude Desktop (recommended default model)

Automated Installation

For quick and easy installation, use the provided installation script:

# Clone the repository
git clone https://github.com/GrizzFuOnYou/master_mcp_server.git
cd master_mcp_server

# Run the installation script
python install.py

The installation script will:

1. Verify Python version compatibility
2. Install all dependencies
3. Create a directory structure
4. Configure environment variables
5. Create platform-specific startup scripts
6. Set up Claude Desktop as the default AI model

Manual Setup

If you prefer manual installation:

1. Clone the repository:

   git clone https://github.com/GrizzFuOnYou/master_mcp_server.git
   cd master_mcp_server
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Configure environment variables:

   cp .env.example .env
   # Edit .env with your preferred settings
   

Usage

Starting the Server

Using Startup Script (Recommended)

After installation:

• Windows: Run start_mcp_server.bat
• Linux/Mac: Run ./start_mcp_server.sh

Manual Start

Run the MCP server:

python startup.py

By default, the server will listen on 0.0.0.0:8000.

Connecting AI Models

Claude Desktop (Default)

Claude Desktop is configured as the default model. To use it:

1. Make sure Claude Desktop is running on your system
2. The server will automatically attempt to connect on startup
3. Claude Desktop should be available at the default location: http://localhost:5000/api

If you need to manually connect:

from mcp_client import MCPClient

# Initialize client
client = MCPClient("http://localhost:8000", "your-secret-api-key")

# Connect to Claude Desktop
result = client.connect_model("claude-desktop", "claude", {"api_url": "http://localhost:5000/api"})
print(f"Connection result: {result}")

Claude Desktop Connection JSON

If you need to manually configure Claude Desktop integration, use the following JSON configuration:

{
  "model_id": "claude-desktop",
  "model_type": "claude",
  "config": {
    "api_url": "http://localhost:5000/api",
    "temperature": 0.7,
    "max_tokens": 1000
  }
}

Ollama Models

To connect to an Ollama model:

from mcp_client import MCPClient

# Initialize client
client = MCPClient("http://localhost:8000", "your-secret-api-key")

# Connect to an Ollama model
result = client.connect_model("llama2", "ollama", {"host": "http://localhost:11434"})
print(f"Connection result: {result}")

Executing System Operations

Once connected, AI models can perform various system operations:

# Execute a command
result = client.execute_system_command("claude-desktop", "echo", ["Hello, World!"])

# Write a file
result = client.write_file("claude-desktop", "test.txt", "This is a test file created by Claude!")

# Read a file
result = client.read_file("claude-desktop", "test.txt")

# Start a program
result = client.start_program("claude-desktop", "notepad.exe")

# Stop a program
result = client.stop_program("claude-desktop", pid)

# Query the AI model
result = client.query_model("claude-desktop", "claude-desktop", "What is the capital of France?")

API Reference

Server Endpoints

| Endpoint | Method | Description | |----------|--------|-------------| | /connect_model | POST | Connect to an AI model | | /disconnect_model/{model_id} | POST | Disconnect from an AI model | | /list_models | GET | List all connected models | | /execute_task | POST | Execute a task requested by an AI model | | /task_status/{task_id} | GET | Get the status of a task |

Client Methods

| Method | Description | |--------|-------------| | connect_model(model_id, model_type, config) | Connect to an AI model | | disconnect_model(model_id) | Disconnect from an AI model | | list_models() | List all connected models | | execute_system_command(model_id, command, args, working_dir, timeout) | Execute a system command | | execute_file_operation(model_id, operation, path, content) | Execute a file operation | | control_program(model_id, action, program_path, args, pid) | Control a program | | query_model(model_id, target_model, prompt) | Query an AI model |

Model Configuration

Claude Desktop Configuration

To connect to Claude Desktop, use the following configuration:

{
  "api_url": "http://localhost:5000/api",
  "temperature": 0.7,
  "max_tokens": 1000
}

Ollama Configuration

To connect to an Ollama model, use the following configuration:

{
  "host": "http://localhost:11434"
}

Security Considerations

IMPORTANT: This server grants AI models significant access to your system. Use with caution.

Security measures implemented:

• API key authentication
• Logging of all operations
• Configurable permissions (coming soon)
• Rate limiting (coming soon)

Troubleshooting

Claude Desktop Connection Issues

If you encounter issues connecting to Claude Desktop:

1. Ensure Claude Desktop is running
2. Verify the API URL (default: http://localhost:5000/api)
3. Check the logs for specific error messages
4. Restart Claude Desktop and try again

Ollama Connection Issues

If you encounter issues connecting to Ollama:

1. Ensure Ollama is running (ollama serve)
2. Verify the model exists (ollama list)
3. Check the API URL (default: http://localhost:11434)
4. Try pulling the model again (ollama pull modelname)

Extension Points

The MCP server can be extended to support:

• Additional AI model backends
• More sophisticated program control
• GUI interaction capabilities
• Web browsing capabilities
• Network operation capabilities

License

MIT

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.