LM Studio MCP Bridge

A Node.js based Model Context Protocol (MCP) bridge that enables Antigravity (and other MCP clients) to interact with locally hosted Large Language Models (LLMs) via LM Studio.

Overview

This bridge acts as a translation layer between the MCP standard and LM Studio's OpenAI-compatible and native administrative APIs. It allows AI assistants to autonomously query, load, and manage local models running on your hardware.

Related MCP server: llmstudio-mcp

Features

• 💬 Dynamic Chat & Vision: Query local LLMs with text and images. Supports structured JSON output, reasoning, and professional inference controls (top_p, top_k, seed, stop, etc.).

• 📂 Privacy-First RAG: Semantic search across local directories using local embeddings.

• 📑 Direct File Interaction: Read, analyze, and query local files directly.

• 🏗️ Model Orchestration: Programmatically load and unload models to manage hardware resources.

• 🤖 Auto-Model Selection: Automatically selects the first available loaded model if none is specified.

• 🏷️ Model Attribution: Every response clearly identifies which model generated the answer.

• ⚡ Async Processing: Offload long-running vision tasks to the background.

• 🏥 System Monitoring: Check CPU/Memory health and bridge configuration.

Available Tools (v2.0.0)

The bridge provides a comprehensive suite of 28 tools categorized for various AI workflows:

🗨️ Core Interaction

• query_local_llm: Standard text generation. Supports Vision, JSON Schema, and expert parameters (top_p, top_k, stop, penalty, seed).

• query_local_llm_stateful: Advanced stateful query using /v1/responses. Supports stateful context, reasoning control, and sampling parameters.

• analyze_local_image: Direct image analysis using local vision models.

• analyze_local_image_async: Start background image analysis (returns a Task ID).

• get_bridge_task_status: Check progress of asynchronous vision tasks.

📁 File & Knowledge (RAG)

• search_local_docs: Semantic vector search across local document directories. Now uses auto-model selection.

• get_local_embeddings: Generate text embeddings for local indexing. Supports batch arrays and auto-model selection.

• query_local_file: Read a file and ask questions about its specific content.

• list_files_in_directory: Browse local file systems.

• read_file_content: Fetch raw content from local files.

🤖 Model Management

• list_local_models: See all loaded and available models (optionally detailed).

• load_local_model: Load a specific model ID into memory/VRAM.

• unload_local_model: Free up resources by unloading models.

🌐 Mesh & Network (LM Link)

• get_lm_link_status: View current network status and all discovered mesh devices.

• manage_lm_link: Administrative control to enable, disable, or rename your local Link node.

• set_preferred_lm_link_device: Programmatically route AI tasks to a specific remote machine.

🛠️ System & Debugging

• get_system_health: Monitor bridge machine CPU and Memory usage.

• check_server_status: Verify connection to the LM Studio API.

• get_bridge_config: View current host, port, and authentication settings.

🖥️ CLI Management (Advanced)

• lms_status: Show the overall health of the LM Studio daemon and server.

• lms_ls: List models currently available on disk (richer than API list).

• lms_ps: List models currently loaded in memory (RAM/VRAM).

• lms_get: Search for or download models from LM Studio Hub / Hugging Face.

• lms_import: Import a local model file (.gguf) into LM Studio.

• lms_server_control: Start, stop, or check the status of the inference server.

• lms_load_cli: Load models with advanced controls (GPU offload, context length).

• lms_log_snapshot: Capture a snapshot of current system logs.

• lms_runtime_control: Manage and update the inference runtime engines (engines list, survey hardware).

Usage Examples

🧱 Structured Data (JSON Schema)

Force the model to return valid JSON following a specific schema.

{
  "prompt": "Generate a random user profile",
  "json_schema": {
    "type": "object",
    "properties": {
      "name": { "type": "string" },
      "age": { "type": "integer" }
    },
    "required": ["name", "age"]
  }
}

{
  "prompt": "What is shown in this architecture diagram?",
  "image_path": "C:/Users/otwo/Desktop/system_init.png"
}

🧠 Stateful Follow-up (Responses API)

Continue a conversation without re-sending history by using a Response ID.

{
  "input": "Can you explain the previous calculation in more detail?",
  "previous_response_id": "resp_987654321",
  "reasoning_effort": "high"
}

Prerequisites

• LM Studio: version 0.3.0+ (with Local Server enabled on port 1234).

• Node.js: v18.0.0 or higher.

• MCP Client: Such as Antigravity, Claude Desktop, or any tool that supports the Model Context Protocol.

Getting Started

1. Installation

Clone this repository and install the required dependencies:

git clone https://github.com/ozwei/lmstudio-mcp-bridge.git
cd lmstudio-mcp-bridge
npm install

2. Configuration

Create a .env file in the root directory (you can copy from .env.example) and fill in your LM Studio details:

LM_HOST=localhost
LM_PORT=1234
LM_API_TOKEN=your_token_here

3. Architecture: Using with LM Link

If you are using LM Link to connect multiple devices:

1. Setup: Run LM Studio on both your "Server" (powerful machine) and "Client" (where you are coding).

2. Connectivity: Enable LM Link to share the server's models with the client.

3. Bridge Placement: Run the lmstudio-mcp-bridge on your Client machine.

4. Proxying: Set LM_HOST=localhost in your .env. The bridge will talk to your local client, which will transparently route requests to the remote models via the secure link.

Data Flow:

graph LR
    A["IDE (Antigravity)"] -- MCP Protocol --> B["MCP Bridge (Local Device)"]
    B -- HTTP/JSON --> C["Local LM Studio Client"]
    C -- Secure Tunnel (LM Link) --> D["Remote LM Studio Server"]
    D -- Inference --> E["GPU / Local LLM"]

    style A fill:#3498db,color:#fff,stroke:#2980b9,stroke-width:2px
    style B fill:#9b59b6,color:#fff,stroke:#8e44ad,stroke-width:2px
    style C fill:#2ecc71,color:#fff,stroke:#27ae60,stroke-width:2px
    style D fill:#e67e22,color:#fff,stroke:#d35400,stroke-width:2px
    style E fill:#e74c3c,color:#fff,stroke:#c0392b,stroke-width:2px

IDE (Antigravity/Claude Code) -> MCP Bridge -> Local LM Studio Client -> LM Link -> Remote LM Studio Server

4. Usage in Antigravity

Add the bridge to your MCP settings:

{
  "mcpServers": {
    "lmstudio-bridge": {
      "command": "node",
      "args": ["C:/absolute/path/to/lmstudio-mcp-bridge/src/index.js"]
    }
  }
}

License

This project is licensed under the MIT License - see the LICENSE file for details.