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Manage local model runtimes with unified discovery, checks, lifecycle control, and inference.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Local AI MCP" yet — see the docs or source repo.
Scan local model runtimes on this machine, such as Ollama and LM Studio, then list available models, providers, versions, ports, and current status grouped by runtime.
A structured inventory of local runtimes and models, including status, source, and connection details.
Evaluate whether this device can run a local model at the Llama 3 class, using CPU, RAM, VRAM, and available backends to provide compatibility, risks, and recommended settings.
A hardware compatibility analysis explaining feasibility, bottlenecks, and recommended model or configuration options.
Use an available local model runtime to run inference: summarize the following meeting notes and report which runtime and model were used. If the preferred provider is unavailable, automatically fall back to another available provider.
The inference result plus metadata on the runtime, model used, and any fallback that occurred.
Run Llama models locally for private, offline AI assistance.
Connect local Ollama to MCP apps for chat, model management, and generation.
Delegate low-risk tasks to a cheaper model with main-agent review.
Use local Ollama models in a terminal MCP client for AI workflows.
Securely let AI read, search, and edit local files with local LLMs.
Connect local Ollama models to MCP clients for discovery and Q&A.