llmstudio-mcp
Related Servers
Alternatives to llmstudio-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityCmaintenanceExposes local LM Studio language models as MCP tools, enabling chat completions and model listing through a local OpenAI-compatible API without requiring API keys.MIT
- FlicenseCqualityDmaintenanceEnables MCP clients to interact with local LLMs via LM Studio, supporting dynamic chat, vision, RAG, file interaction, and model orchestration.28-
- FlicenseAqualityBmaintenanceEnables MCP-compatible clients to use locally running LM Studio models as tools for sending prompts, listing available models, and checking server status. It automatically handles WSL2 networking and follows the currently loaded model without reconfiguration.3-
- AlicenseNot gradedqualityDmaintenanceMCP server that provides tools to interact with the LiteLLM proxy API, enabling LLM completions, embeddings, image generation, and admin operations.7 npmMIT
- FlicenseNot gradedqualityDmaintenanceLocal MCP server that exposes fixed tools for GPT, Claude, and Gemini while routing to any OpenAI-compatible chat completions backend with independent configuration per target.1-
- FlicenseNot gradedqualityFmaintenanceAn MCP server that allows Claude to interact with local LLMs running in LM Studio, providing access to list models, generate text, and use chat completions through local models.13-
TDQS
Scored across 9 tools
Each tool has a distinct purpose: chat, complete, and embed for inference; list_models, get_model, load_model, unload_model for model management; raw_request as an escape hatch. No functional overlap.
Consistent verb_noun pattern (e.g., get_loaded_models, list_models, load_model) with simple action verbs for inference (chat, complete, embed). All lowercase with underscores; clear and predictable.
9 tools cover the core server management lifecycle: model discovery, loading/unloading, inference, and a raw_request for extensibility. Neither too few nor too many for the domain.
Covers all essential operations: model listing, loading, inference (chat/complete/embed), and unloading. Missing model download/delete is a minor gap, but raw_request can handle edge cases.