local-llm-mcp
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Alternatives to local-llm-mcp
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AlicenseAqualityAmaintenanceUnified MCP server for managing local model runtimes (Ollama, LM Studio, etc.), enabling provider-agnostic discovery, lifecycle management, hardware-fit checks, and delegated inference.1618Creative Commons Attribution Non Commercial No Derivatives 4.0 International- AlicenseAqualityDmaintenanceEnables seamless integration between Ollama's local LLM models and MCP-compatible applications, supporting model management and chat interactions.13889170AGPL 3.0
- FlicenseCqualityDmaintenanceEnables MCP clients to interact with local LLMs via LM Studio, supporting dynamic chat, vision, RAG, file interaction, and model orchestration.28-
- AlicenseNot gradedqualityDmaintenanceEnables listing, loading, unloading, and configuring models in LM Studio via MCP tools, with support for TTL and draft model settings.6MIT
- FlicenseAqualityCmaintenanceMCP server that connects LLM agents to a local LM Studio instance, enabling model management, OpenAI-compatible chat completions, text completions, and embeddings through a set of tools.91-
- 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
TDQS
Scored across 9 tools
Most tools target distinct actions: ping, runtime_info, list_models, start/stop/status, chat. The only notable overlap is between estimate_vram and suggest_gpu_layers, since estimate_vram already returns a suggested_gpu_layers field; an agent could be unsure which to call for layer suggestions. Descriptions help but this pair remains somewhat confused.
All names use snake_case, which is consistent. However, the pattern is mixed: some are verb_noun (list_models, start_server, estimate_vram), while others are noun phrases (runtime_info, server_status) or simple verbs (ping, chat). Minor deviations keep it from a perfect verb_noun pattern.
Nine tools is well-scoped for local LLM server management. The set covers lifecycle, inspection, resource estimation, and inference without obvious bloat. Each tool has a clear role, making the count appropriate.
The surface covers core workflows: start/stop server, status, model listing, VRAM estimation, GPU layer suggestion, and chat. Minor gaps exist, such as no tool to stream or cancel an in-progress chat, no model download, and no configuration update. These are workable around but leave the surface slightly incomplete.