Content
# llmstudio-mcp
[Polski](README.pl.md)
MCP server exposing a local [LM Studio](https://lmstudio.ai) instance to LLM agents — list / load / unload models, run chat and text completions, generate embeddings, and reach any other LM Studio REST endpoint via a raw escape hatch.
## Table of contents
- [Tools](#tools)
- [Environment variables](#environment-variables)
- [Prerequisites](#prerequisites)
- [Wiring it up](#wiring-it-up)
- [Local run](#local-run)
## Tools
| Tool | Parameters | Description |
|------|-----------|-------------|
| `list_models` | `type: "all" \| "llm" \| "embedding" \| "vlm" = "all"` | Lists downloaded models with metadata (type, state, context length, quant) |
| `get_model` | `model_key: str` | Info about a single model |
| `get_loaded_models` | — | Models currently resident in memory |
| `load_model` | `model_key, context_length?, flash_attention?, offload_kv_cache_to_gpu?, num_experts?, eval_batch_size?` | Loads a model into memory |
| `unload_model` | `instance_id: str` | Frees a model from memory |
| `chat` | `model, messages, temperature?, max_tokens?, top_p?, top_k?, stop?, stream?, tools?` | OpenAI-compatible chat completion (`/v1/chat/completions`) |
| `complete` | `model, prompt, temperature?, max_tokens?, top_p?, top_k?, stop?, stream?` | OpenAI-compatible text completion (`/v1/completions`) |
| `embed` | `model, input, normalize=false` | OpenAI-compatible embeddings (`/v1/embeddings`) |
| `raw_request` | `method, path, json_body?` | Escape hatch for any LM Studio REST endpoint |
## Environment variables
| Variable | Required | Default | Description |
|---------|----------|---------|-------------|
| `LMSTUDIO_MCP_BASE_URL` | no | `http://localhost:1234` | LM Studio server URL |
| `LMSTUDIO_MCP_API_KEY` | no | empty | API token (only if you enabled token auth in LM Studio's Developer tab) |
| `LMSTUDIO_MCP_TIMEOUT` | no | `60.0` | HTTP timeout in seconds; raise for slow models or large generations |
## Prerequisites
LM Studio running with its API server enabled — either the desktop app with
the Developer tab toggle on, or `lms server start` from the CLI. At least one
model downloaded (`lms get <model>`).
## Wiring it up
Only requirement: `uv` (https://docs.astral.sh/uv/). Nothing else to install.
### Claude Code
```
claude mcp add llmstudio-mcp -- uvx --from git+https://github.com/dam2452/llmstudio-mcp.git llmstudio-mcp
```
If you enabled API token auth in LM Studio:
```
claude mcp add llmstudio-mcp -e LMSTUDIO_MCP_API_KEY=<token> -- uvx --from git+https://github.com/dam2452/llmstudio-mcp.git llmstudio-mcp
```
### Claude Desktop / other MCP client
```json
{
"mcpServers": {
"llmstudio-mcp": {
"command": "uvx",
"args": ["--from", "git+https://github.com/dam2452/llmstudio-mcp.git", "llmstudio-mcp"]
}
}
}
```
After pushing a new version: `uv cache clean` and restart the client.
## Local run
```
uv run --directory . llmstudio-mcp
```
Tests (manual):
```
uv run --directory . --with pytest pytest test/
```
