Ollama / vLLM Bridge MCP server

Local-AI MCP servers: Ollama & vLLM model management and local LLM access for Claude, ChatGPT and other MCP clients

Two self-hostable Model Context Protocol (MCP) servers for running and steering your own local language models: manage an Ollama/vLLM model machine, and query a local LLM (Ollama, vLLM or an OpenAI-compatible backend) for language work with schema-validated JSON output. Self-hosted and privacy-first: the models and their machine stay on your own hardware, and neither server process reaches out to a public address on its own. Version 1.0.0, single-operator tool, actively used by the author.

Both servers speak the Model Context Protocol (MCP), so an AI tool such as Claude or ChatGPT can use them. Each server's own traffic goes only to your own computer or your local network; a start-up guard rejects public addresses. Installing the two direct dependencies needs the internet once, at setup time. Fetching a model is a separate matter: in mcp-modelmanager, calling fetchmodel is a deliberate, tool-triggered connection from your model machine to a public model registry, at whatever point you invoke it, not something limited to setup time. The default source="ollama" makes the machine's own Ollama daemon pull from registry.ollama.ai for plain model names, or from whatever other registry a prefixed name such as hf.co/user/repo points at; source="huggingface" instead reaches Hugging Face directly. switchservice never reaches either registry itself; it refuses and asks for fetch_model first if the model is not already on the machine. Each server stands on its own: pick one, or run both.

A caveat: the AI tool you drive these servers with (Claude, ChatGPT, etc.) is itself a hosted service in the usual case. The instructions you give it and the tool inputs and results it handles pass through that provider, exactly as any other MCP call would. "Local" here means the models, the model machine and its data; it is not a claim that nothing you type ever reaches a third party. Drive them with a local model if that matters to you.

The two servers

mcp-modelmanager -> mcp-modelmanager/

Manage your own model machine through an AI tool: read state (GPU memory, disk, running service, containers), pull and remove models, switch the vLLM inference service to a different model, build custom Ollama variants and run LoRA training. There is deliberately no tool for free remote control: every operation is a fixed template and every parameter first passes an allowlist, checked independently on both sides. This server ships both halves: the client/server code the AI tool talks to, and the machine-side scripts, wrapper and network-fence templates that run on and lock down the model machine.

mcp-ollama-vllm -> mcp-ollama-vllm/

Shortened. The full README is on GitHub.