LM Studio MCP Server

A Model Context Protocol (MCP) server for LM Studio that enables model management through standardized tools.

Features

• 📋 List Models - View all available models and their current state

• 🚀 Load Models - Load models into memory with configurable TTL

• 🛑 Unload Models - Immediately unload models from memory

• ⚙️ Configure Models - Adjust model settings like TTL and draft models

• 📊 Model Details - Get detailed information about specific models

Related MCP server: Agentforce MCP Integration Server

Prerequisites

• Node.js >= 18.0.0

• LM Studio running with local server enabled

• LM Studio local server running on port 1234 (default) or custom port

Installation

npm install
npm run build

Quickstart (Build & Run)

Follow these steps to build and run the MCP server locally.

1. Install dependencies and build the project:

npm install
npm run build

2. Start the server (uses the compiled files in dist):

npm start

3. The server writes MCP communication to stdout and logs to stderr.

Environment variable tips:

• Default LM Studio URL: http://localhost:1234.

• To use a custom LM Studio URL, set LM_STUDIO_BASE_URL before starting.

PowerShell (Windows) example:

$env:LM_STUDIO_BASE_URL = "http://localhost:1234"
npm start

Command Prompt (Windows) example:

set LM_STUDIO_BASE_URL=http://localhost:1234 && npm start

macOS / Linux example:

LM_STUDIO_BASE_URL="http://localhost:1234" npm start

Development workflow:

• Rebuild on change (in one terminal): npm run watch

• Run the server (in another terminal): npm run dev (starts Node with the inspector)

You can also run the compiled script directly with node dist/index.js if preferred.

Configuration

LM Studio Setup

1. Open LM Studio

2. Go to the Developer tab

3. Enable the local server (default port: 1234)

4. Optionally enable "Serve on Local Network" if accessing remotely

Environment Variables

• LM_STUDIO_BASE_URL - Base URL for LM Studio API (default: http://localhost:1234)

Usage

With Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "lmstudio": {
      "command": "node",
      "args": ["/path/to/lmstudio-mcp/dist/index.js"],
      "env": {
        "LM_STUDIO_BASE_URL": "http://localhost:1234"
      }
    }
  }
}

With Other MCP Clients

Run the server directly:

node dist/index.js

The server communicates over stdio following the MCP protocol.

Available Tools

list_models

List all available models with their current state (loaded/not-loaded).

Parameters: None

Example Response:

[
  {
    "id": "lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF",
    "type": "llm",
    "publisher": "Meta",
    "architecture": "llama",
    "state": "loaded",
    "max_context_length": 8192
  }
]

get_model_details

Get detailed information about a specific model.

Parameters:

• model_id (string, required) - The ID of the model

Example:

{
  "model_id": "lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF"
}

load_model

Load a model into memory with configurable Time-To-Live.

Parameters:

• model_id (string, required) - The ID of the model to load

• ttl (number, optional) - Time-To-Live in seconds before auto-unload (default: 3600)

Example:

{
  "model_id": "lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF",
  "ttl": 7200
}

unload_model

Unload a model from memory immediately.

Parameters:

• model_id (string, required) - The ID of the model to unload

Example:

{
  "model_id": "lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF"
}

configure_model

Configure model settings such as TTL and draft model for speculative decoding.

Parameters:

• model_id (string, required) - The ID of the model to configure

• ttl (number, optional) - Time-To-Live in seconds

• draft_model (string, optional) - Draft model ID for speculative decoding

Example:

{
  "model_id": "lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF",
  "ttl": 1800,
  "draft_model": "small-draft-model"
}

How It Works

LM Studio uses JIT (Just-In-Time) model loading. Models are loaded on-demand when inference requests are made:

• Loading: Making an inference request automatically loads the model with the specified TTL

• Unloading: Models auto-unload after TTL expires, or immediately when TTL is set to 0

• Configuration: Model settings are applied through inference request parameters

Development

Build

npm run build

Watch Mode

npm run watch

Debug

npm run dev

API Reference

This server interfaces with the LM Studio Developer API:

• GET /api/v0/models - List all available models

• GET /api/v0/models/{model} - Get model details

• POST /api/v0/chat/completions - Used for loading/configuring models

Troubleshooting

Connection Refused

• Ensure LM Studio is running

• Verify the local server is enabled in Developer settings

• Check that port 1234 (or custom port) is accessible

Model Not Found

• Verify the model ID is correct using list_models

• Ensure the model is downloaded in LM Studio

Model Won't Load

• Check available system memory

• Verify model compatibility with your system

• Review LM Studio logs for errors

License

MIT

Contributing

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

Links

• LM Studio

• LM Studio Developer Docs

• Model Context Protocol