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llmstudio-mcp

by dam2452
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Python
Local

llmstudio-mcp

Polski

MCP server exposing a local LM Studio 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

Related MCP server: FastMCP

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

{
  "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/
Install Server
F
license - not found
A
quality
C
maintenance

How are these scores calculated?

Maintenance

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–Response time
–Release cycle
–Releases (12mo)
Commit activity

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