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Manage LM Studio models via MCP for loading, unloading, and configuration.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "LM Studio MCP Server" yet — see the docs or source repo.
Use the LM Studio MCP Server to list all available models, including model name, loaded status, and key configuration details.
A list of available models with each model’s status and basic configuration summary.
Use the LM Studio MCP Server to load a specified model, set TTL to 30 minutes, and enable draft model settings.
A result confirming the model was loaded and the TTL and draft model settings were applied.
Use the LM Studio MCP Server to unload the currently idle model that is no longer needed, and tell me whether the operation succeeded.
A result indicating whether the target model was successfully unloaded and released.
Manage MCP server configurations with listing, toggling, and version control.
Offload non-critical LLM tasks to your own model to save premium quota.
Manage local model runtimes with unified discovery, checks, lifecycle control, and inference.
Delegate low-risk tasks to a cheaper model with main-agent review.
Access multiple AI providers in one terminal for generation, search, and comparison.
Run Llama models locally for private, offline AI assistance.