llmstudio-mcp
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Alternatives to llmstudio-mcp
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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
- AlicenseNot gradedqualityAmaintenanceEnables MCP-compatible applications to manage and run local or cloud language models, perform chat, text generation, and embeddings, and use web search and fetch tools through MCP.1,165 npmAGPL 3.0
- AlicenseCqualityDmaintenanceEnables MCP clients to interact with local LLMs via LM Studio, supporting dynamic chat, vision, RAG, file interaction, and model orchestration.28MIT
- AlicenseNot gradedqualityAmaintenanceEnables MCP-compatible clients to manage and run local or cloud LLMs, generate completions and embeddings, and perform web search/fetch through exposed tools.1,165 npmAGPL 3.0
- 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 gradedqualityAmaintenanceEnables MCP-compatible applications to manage and use local or cloud-hosted models, including listing, pulling, generating, chatting, embeddings, and web search/fetch through a unified tool interface.1,165 npmAGPL 3.0
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.