MCP Server with Local LLM

A Model Context Protocol (MCP) server that integrates with your local Large Language Model, providing tools and resources that can be used by MCP-compatible clients like Claude Desktop, IDEs, and other AI assistants.

Features

• Local LLM Integration: Uses your local language model (default: Qwen3-8B) for all operations
• MCP-Compatible: Implements the Model Context Protocol specification
• Multiple Tools: Provides various tools for different use cases
• Resource Access: Exposes model information and conversation history
• Memory Efficient: Uses 4-bit quantization for optimal performance

Available Tools

1. chat_with_llm

Basic chat interface with the local LLM.

Parameters:

• message (required): The message to send to the LLM
• system_prompt (optional): System prompt to guide behavior
• temperature (optional): Temperature for generation (0-1, default: 0.7)
• max_tokens (optional): Maximum tokens to generate (default: 512)

2. analyze_code

Analyze and review code using the local LLM.

Parameters:

• code (required): The code to analyze
• language (optional): Programming language
• analysis_type (optional): Type of analysis (review, explain, optimize, debug)

3. generate_text

Generate text content in various styles and lengths.

Parameters:

• prompt (required): The prompt for text generation
• style (optional): Writing style (formal, casual, technical, creative)
• length (optional): Desired length (short, medium, long)

4. translate_text

Translate text between languages.

Parameters:

• text (required): Text to translate
• target_language (required): Target language
• source_language (optional): Source language (auto-detect if not provided)

5. summarize_content

Summarize content in different formats.

Parameters:

• content (required): Content to summarize
• summary_type (optional): Type of summary (brief, detailed, bullet_points)
• max_length (optional): Maximum length in words (default: 150)

Available Resources

1. llm://model/info

Information about the loaded local LLM model.

2. llm://conversations/history

History of conversations with the LLM.

3. llm://system/status

Current system status and resource usage.

Installation

1. Clone or download this repository to your local machine.

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up environment variables (create a .env file):

   MODEL_NAME=Qwen/Qwen3-8B
   MAX_NEW_TOKENS=512
   TEMPERATURE=0.7
   

4. Ensure you have sufficient hardware:

   • GPU with at least 8GB VRAM (recommended)
   • 16GB+ system RAM
   • CUDA-compatible GPU (for optimal performance)

Usage

Running the Server

1. Start the MCP server:

   python mcp_server.py
   

2. The server will:

   • Load the specified model (Qwen3-8B by default)
   • Initialize the MCP server
   • Wait for JSON-RPC requests via stdin/stdout

Integrating with Claude Desktop

To use this server with Claude Desktop, add it to your MCP configuration:

1. Create or edit your Claude Desktop MCP configuration file:

   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json

2. Add the server configuration:

   {
     "mcpServers": {
       "local-llm": {
         "command": "python",
         "args": ["/Users/sazzad/Documents/mcp-server/mcp_server.py"],
         "env": {
           "MODEL_NAME": "Qwen/Qwen3-8B"
         }
       }
     }
   }
   

3. Restart Claude Desktop to load the new server.

Example Usage in Claude Desktop

Once configured, you can use the tools in Claude Desktop:

Please use the local LLM to analyze this Python code:

Claude will then use the analyze_code tool to get analysis from your local LLM.

Configuration

Environment Variables

• MODEL_NAME: HuggingFace model name (default: "Qwen/Qwen3-8B")
• MAX_NEW_TOKENS: Maximum tokens to generate (default: 512)
• TEMPERATURE: Default temperature for generation (default: 0.7)

Supported Models

This server works with most HuggingFace transformers models that support:

• Chat templates
• 4-bit quantization via BitsAndBytesConfig
• Causal language modeling

Popular choices:

• Qwen/Qwen3-8B (default)
• microsoft/DialoGPT-medium
• meta-llama/Llama-2-7b-chat-hf
• mistralai/Mistral-7B-Instruct-v0.1

Hardware Requirements

Minimum Requirements

• 8GB RAM
• 4GB GPU VRAM (with 4-bit quantization)
• 10GB disk space (for model storage)

Recommended Requirements

• 16GB+ RAM
• 8GB+ GPU VRAM
• CUDA-compatible GPU
• SSD storage for faster model loading

Troubleshooting

Common Issues

1. Out of Memory Error:

   • Reduce MAX_NEW_TOKENS
   • Use a smaller model
   • Ensure 4-bit quantization is working

2. Model Loading Fails:

   • Check internet connection for initial download
   • Verify model name is correct
   • Ensure sufficient disk space

3. CUDA Errors:

   • Update CUDA drivers
   • Check GPU compatibility
   • Fall back to CPU if needed

Performance Optimization

1. Use GPU acceleration:

   pip install torch --index-url https://download.pytorch.org/whl/cu118
   

2. Enable Flash Attention (if compatible):

   pip install flash-attn
   

3. Adjust batch size and sequence length based on your hardware.

Development

Project Structure

mcp-server/
├── mcp_server.py          # Main MCP server implementation
├── mcp-server.py          # Legacy FastAPI server (keep for reference)
├── requirements.txt       # Python dependencies
├── mcp/                   # MCP protocol implementation
│   ├── __init__.py
│   ├── server.py         # MCP server base
│   └── types.py          # MCP protocol types
├── .env                  # Environment variables (create this)
└── README.md            # This file

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

For issues and questions:

1. Check the troubleshooting section
2. Review the configuration
3. Open an issue on GitHub