What is Lmstudio Mcp

LMStudio-MCP is a Model Control Protocol (MCP) server that facilitates communication between Claude and locally running LLM models via LM Studio.

Use cases

Use cases include leveraging Claude’s interface to interact with private LLM models, generating text completions for applications, and testing model performance in a controlled environment.

How to use

To use LMStudio-MCP, clone the repository, install the required Python packages, configure the MCP settings, and ensure LM Studio is running with a model loaded. Start the server and interact with it through Claude.

Key features

Key features include health checks for the LM Studio API, listing available models, retrieving the currently loaded model, and generating completions using local models.

Where to use

LMStudio-MCP can be used in fields such as natural language processing, machine learning model deployment, and AI research where local model interaction is required.

LMStudio-MCP

A Model Control Protocol (MCP) server that allows Claude to communicate with locally running LLM models via LM Studio.

Overview

LMStudio-MCP creates a bridge between Claude (with MCP capabilities) and your locally running LM Studio instance. This allows Claude to:

• Check the health of your LM Studio API
• List available models
• Get the currently loaded model
• Generate completions using your local models

This enables you to leverage your own locally running models through Claude’s interface, combining Claude’s capabilities with your private models.

Prerequisites

• Python 3.7+
• LM Studio installed and running locally with a model loaded
• Claude with MCP access
• Required Python packages (see Installation)

🚀 Quick Installation

One-Line Install (Recommended)

curl -fsSL https://raw.githubusercontent.com/infinitimeless/LMStudio-MCP/main/install.sh | bash

Manual Installation Methods

1. Local Python Installation

git clone https://github.com/infinitimeless/LMStudio-MCP.git
cd LMStudio-MCP
pip install requests "mcp[cli]" openai

2. Docker Installation

# Using pre-built image
docker run -it --network host ghcr.io/infinitimeless/lmstudio-mcp:latest

# Or build locally
git clone https://github.com/infinitimeless/LMStudio-MCP.git
cd LMStudio-MCP
docker build -t lmstudio-mcp .
docker run -it --network host lmstudio-mcp

3. Docker Compose

git clone https://github.com/infinitimeless/LMStudio-MCP.git
cd LMStudio-MCP
docker-compose up -d

For detailed deployment instructions, see DOCKER.md.

MCP Configuration

Quick Setup

Using GitHub directly (simplest):

{
  "lmstudio-mcp": {
    "command": "uvx",
    "args": [
      "https://github.com/infinitimeless/LMStudio-MCP"
    ]
  }
}

Using local installation:

{
  "lmstudio-mcp": {
    "command": "/bin/bash",
    "args": [
      "-c",
      "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
    ]
  }
}

Using Docker:

{
  "lmstudio-mcp-docker": {
    "command": "docker",
    "args": [
      "run",
      "-i",
      "--rm",
      "--network=host",
      "ghcr.io/infinitimeless/lmstudio-mcp:latest"
    ]
  }
}

For complete MCP configuration instructions, see MCP_CONFIGURATION.md.

Usage

1. Start LM Studio and ensure it’s running on port 1234 (the default)
2. Load a model in LM Studio
3. Configure Claude MCP with one of the configurations above
4. Connect to the MCP server in Claude when prompted

Available Functions

The bridge provides the following functions:

• health_check(): Verify if LM Studio API is accessible
• list_models(): Get a list of all available models in LM Studio
• get_current_model(): Identify which model is currently loaded
• chat_completion(prompt, system_prompt, temperature, max_tokens): Generate text from your local model

Deployment Options

This project supports multiple deployment methods:

Known Limitations

• Some models (e.g., phi-3.5-mini-instruct_uncensored) may have compatibility issues
• The bridge currently uses only the OpenAI-compatible API endpoints of LM Studio
• Model responses will be limited by the capabilities of your locally loaded model

Troubleshooting

API Connection Issues

If Claude reports 404 errors when trying to connect to LM Studio:

• Ensure LM Studio is running and has a model loaded
• Check that LM Studio’s server is running on port 1234
• Verify your firewall isn’t blocking the connection
• Try using “127.0.0.1” instead of “localhost” in the API URL if issues persist

Model Compatibility

If certain models don’t work correctly:

• Some models might not fully support the OpenAI chat completions API format
• Try different parameter values (temperature, max_tokens) for problematic models
• Consider switching to a more compatible model if problems persist

For detailed troubleshooting help, see TROUBLESHOOTING.md.

🐳 Docker & Containerization

This project includes comprehensive Docker support:

• Multi-architecture images (AMD64, ARM64/Apple Silicon)
• Automated builds via GitHub Actions
• Pre-built images available on GitHub Container Registry
• Docker Compose for easy deployment
• Kubernetes manifests for production deployments

See DOCKER.md for complete containerization documentation.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

MIT

Acknowledgements

This project was originally developed as “Claude-LMStudio-Bridge_V2” and has been renamed and open-sourced as “LMStudio-MCP”.

🌟 If this project helps you, please consider giving it a star!